Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points. - Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (Neural Networks, 1994)

NLq theory: unifications in the theory of neural networks, systems and control. - Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (ESANN, 1995)

Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control. - Johan A. K. Suykens; Joos Vandewalle (ICNN, 1995)

On the identification of a chaotic system by means of recurrent neural state space models. - Johan A. K. Suykens; Joos Vandewalle (ICNN, 1995)

Generalized Cellular Neural Networks Represented in he NLq Framework. - Johan A. K. Suykens; Joos Vandewalle (ISCAS, 1995)

Artificial neural networks for modelling and control of non-linear systems. - Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (Kluwer, 1996)

Discrete Time Interconnected Cellular Neural Networks Within NLq Theory. - Johan A. K. Suykens; Joos Vandewalle (Int. J. Circuit Theory Appl., 1996)

Modelling the Belgian Gas Consumption Using Neural Networks. - Johan A. K. Suykens; Philippe Lemmerling; Wouter Favoreel; Bart De Moor; M. Crepel; P. Briol (Neural Process. Lett., 1996)

Robust NLq neural control theory. - Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (ICNN, 1997)

NLq Theory: A Neural Control Framework with Global Asymptotic Stability Criteria. - Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (Neural Networks, 1997)

NLq theory: checking and imposing stability of recurrent neural networks for nonlinear modeling. - Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (IEEE Trans. Signal Process., 1997)

Improved generalization ability of neurocontrollers by imposing NLq stability constraints. - Johan A. K. Suykens; Joos Vandewalle (ESANN, 1998)

Application of NLq Neural Control Theory to a Ball and Beam System. - Herman Verrelst; Kristel Van Acker; Johan A. K. Suykens; Bart Motmans; Bart De Moor; Joos Vandewalle (Eur. J. Control, 1998)

On-Line Learning Fokker-Planck Machine. - Johan A. K. Suykens; Herman Verrelst; Joos Vandewalle (Neural Process. Lett., 1998)

Multiclass least squares support vector machines. - Johan A. K. Suykens; Joos Vandewalle (IJCNN, 1999)

Continuous time NLq theory: absolute stability criteria. - Johan A. K. Suykens; Joos Vandewalle (IJCNN, 1999)

On the realization of n-scroll attractors. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (ISCAS (5), 1999)

Chaos control using least‐squares support vector machines. - Johan A. K. Suykens; Joos Vandewalle (Int. J. Circuit Theory Appl., 1999)

Least Squares Support Vector Machine Classifiers. - Johan A. K. Suykens; Joos Vandewalle (Neural Process. Lett., 1999)

Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity. - Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (IEEE Trans. Autom. Control., 1999)

Training multilayer perceptron classifiers based on a modified support vector method. - Johan A. K. Suykens; Joos Vandewalle (IEEE Trans. Neural Networks, 1999)

Imposing stability in subspace identification by regularization. - Tony Van Gestel; Johan A. K. Suykens; Paul Van Dooren; Bart De Moor (CDC, 2000)

Sparse least squares Support Vector Machine classifiers. - Johan A. K. Suykens; Lukas Lukas; Joos Vandewalle (ESANN, 2000)

The K.U.Leuven competition data: a challenge for advanced neural network techniques. - Johan A. K. Suykens; Joos Vandewalle (ESANN, 2000)

Sparse approximation using least squares support vector machines. - Johan A. K. Suykens; Lukas Lukas; Joos Vandewalle (ISCAS, 2000)

An empirical assessment of kernel type performance for least squares support vector machine classifiers. - Bart Baesens; Stijn Viaene; Tony Van Gestel; Johan A. K. Suykens; Guido Dedene; Bart De Moor; Jan Vanthienen (KES, 2000)

Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. - Stijn Viaene; Bart Baesens; Tony Van Gestel; Johan A. K. Suykens; Dirk Van den Poel; Jan Vanthienen; Bart De Moor; Guido Dedene (PKDD, 2000)

Chaos Synchronization: a Lagrange Programming Network Approach. - Johan A. K. Suykens; Joos Vandewalle (Int. J. Bifurc. Chaos, 2000)

Robust local stability of multilayer recurrent neural networks. - Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (IEEE Trans. Neural Networks Learn. Syst., 2000)

Automatic relevance determination for Least Squares Support Vector Machines classifiers. - Tony Van Gestel; Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (ESANN, 2001)

Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. - Tony Van Gestel; Johan A. K. Suykens; Jos De Brabanter; Bart De Moor; Joos Vandewalle (ICANN, 2001)

Support Vector Machines: A Nonlinear Modelling and Control Perspective. - Johan A. K. Suykens (Eur. J. Control, 2001)

Intelligence and Cooperative Search by Coupled Local Minimizers. - Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (Int. J. Bifurc. Chaos, 2001)

Master-Slave Synchronization of Lur'e Systems with Time-Delay. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (Int. J. Bifurc. Chaos, 2001)

Knowledge discovery in a direct marketing case using least squares support vector machines. - Stijn Viaene; Bart Baesens; Tony Van Gestel; Johan A. K. Suykens; Dirk Van den Poel; Jan Vanthienen; Bart De Moor; Guido Dedene (Int. J. Intell. Syst., 2001)

Improved Long-Term Temperature Prediction by Chaining of Neural Networks. - Michel Duhoux; Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (Int. J. Neural Syst., 2001)

Optimal control by least squares support vector machines. - Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (Neural Networks, 2001)

Identification of stable models in subspace identification by using regularization. - Tony Van Gestel; Johan A. K. Suykens; Paul Van Dooren; Bart De Moor (IEEE Trans. Autom. Control., 2001)

Financial time series prediction using least squares support vector machines within the evidence framework. - Tony Van Gestel; Johan A. K. Suykens; Dirk-Emma Baestaens; Annemie Lambrechts; Gert R. G. Lanckriet; Bruno Vandaele; Bart De Moor; Joos Vandewalle (IEEE Trans. Neural Networks, 2001)

Least Squares Support Vector Machines - Johan A. K. Suykens; Tony Van Gestel; Jos De Brabanter; Bart De Moor; Joos Vandewalle (World Scientific, 2002)

Prediction of mental development of preterm newborns at birth time using LS-SVM. - Lieveke Ameye; Chuan Lu; Lukas Lukas; Jos De Brabanter; Johan A. K. Suykens; Sabine Van Huffel; Hans Daniels; Gunnar Naulaers; Hugo Devlieger (ESANN, 2002)

The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals. - Lukas Lukas; Andy Devos; Johan A. K. Suykens; Leentje Vanhamme; Sabine Van Huffel; Anne Rosemary Tate; Carles Majós; Carles Arús (ESANN, 2002)

Robust Cross-Validation Score Function for Non-linear Function Estimation. - Jos De Brabanter; Kristiaan Pelckmans; Johan A. K. Suykens; Joos Vandewalle (ICANN, 2002)

Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models. - Bart Hamers; Johan A. K. Suykens; Bart De Moor (ICANN, 2002)

Families of scroll Grid attractors. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle; Serdar Özoguz (Int. J. Bifurc. Chaos, 2002)

Weighted least squares support vector machines: robustness and sparse approximation. - Johan A. K. Suykens; Jos De Brabanter; Lukas Lukas; Joos Vandewalle (Neurocomputing, 2002)

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. - Tony Van Gestel; Johan A. K. Suykens; Gert R. G. Lanckriet; Annemie Lambrechts; Bart De Moor; Joos Vandewalle (Neural Comput., 2002)

Multiclass LS SVMs Moderated Outputs and Coding Decoding Schemes. - Tony Van Gestel; Johan A. K. Suykens; Gert R. G. Lanckriet; Annemie Lambrechts; Bart De Moor; Joos Vandewalle (Neural Process. Lett., 2002)

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. - Chuan Lu; Tony Van Gestel; Johan A. K. Suykens; Sabine Van Huffel; Dirk Timmerman; Ignace Vergote (AIME, 2003)

Least squares support vector machines and primal space estimation. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (CDC, 2003)

Bankruptcy prediction with least squares support vector machine classifiers. - Tony Van Gestel; Bart Baesens; Johan A. K. Suykens; Marcelo Espinoza; Dirk-Emma Baestaens; Jan Vanthienen; Bart De Moor (CIFEr, 2003)

Kernel PLS variants for regression. - Luc Hoegaerts; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (ESANN, 2003)

Coupled chaotic simulated annealing processes. - Johan A. K. Suykens; Müstak E. Yalçin; Joos Vandewalle (ISCAS (3), 2003)

Variogram based noise variance estimation and its use in kernel based regression. - Kristiaan Pelckmans; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (NNSP, 2003)

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. - Chuan Lu; Tony Van Gestel; Johan A. K. Suykens; Sabine Van Huffel; Ignace Vergote; Dirk Timmerman (Artif. Intell. Medicine, 2003)

Benchmarking state-of-the-art classification algorithms for credit scoring. - Bart Baesens; Tony Van Gestel; Stijn Viaene; M. Stepanova; Johan A. K. Suykens; Jan Vanthienen (J. Oper. Res. Soc., 2003)

Identification of positive real models in subspace identification by using regularization. - Ivan Goethals; Tony Van Gestel; Johan A. K. Suykens; Paul Van Dooren; Bart De Moor (IEEE Trans. Autom. Control., 2003)

A support vector machine formulation to PCA analysis and its kernel version. - Johan A. K. Suykens; Tony Van Gestel; Joos Vandewalle; Bart De Moor (IEEE Trans. Neural Networks, 2003)

Partially linear models and least squares support vector machines. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (CDC, 2004)

Linear MPC with time-varying terminal cost using sparse convex combinations and bisection search. - Bert Pluymers; Johan A. K. Suykens; Bart De Moor (CDC, 2004)

Robust finite-horizon MPC using optimal worst-case closed-loop predictions. - Bert Pluymers; Johan A. K. Suykens; Bart De Moor (CDC, 2004)

Sparse LS-SVMs using additive regularization with a penalized validation criterion. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (ESANN, 2004)

A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines. - Luc Hoegaerts; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (ICONIP, 2004)

Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (ICONIP, 2004)

Primal space sparse kernel partial least squares regression for large scale problems. - Luc Hoegaerts; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (IJCNN, 2004)

Regularization constants in LS-SVMs: a fast estimate via convex optimization. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (IJCNN, 2004)

A double scroll based true random bit generator. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (ISCAS (4), 2004)

Learning from General Label Constraints. - Tijl De Bie; Johan A. K. Suykens; Bart De Moor (SSPR/SPR, 2004)

Brain tumor classification based on long echo proton MRS signals. - Lukas Lukas; Andy Devos; Johan A. K. Suykens; Leentje Vanhamme; Franklyn A. Howe; Carles Majós; Àngel Moreno-Torres; M. Van Der Graaf; Anne Rosemary Tate; Carles Arús; Sabine Van Huffel (Artif. Intell. Medicine, 2004)

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. - Nathalie Pochet; Frank De Smet; Johan A. K. Suykens; Bart De Moor (Bioinform., 2004)

Benchmarking Least Squares Support Vector Machine Classifiers. - Tony Van Gestel; Johan A. K. Suykens; Bart Baesens; Stijn Viaene; Jan Vanthienen; Guido Dedene; Bart De Moor; Joos Vandewalle (Mach. Learn., 2004)

Toward CNN chip-specific robustness. - Samuel Xavier de Souza; Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (IEEE Trans. Circuits Syst. I Regul. Pap., 2004)

True random bit generation from a double-scroll attractor. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (IEEE Trans. Circuits Syst. I Regul. Pap., 2004)

The efficient computation of polyhedral invariant sets for linear systems with polytopic uncertainty. - Bert Pluymers; John Anthony Rossiter; Johan A. K. Suykens; Bart De Moor (ACC, 2005)

Interpolation based MPC for LPV systems using polyhedral invariant sets. - Bert Pluymers; John Anthony Rossiter; Johan A. K. Suykens; Bart De Moor (ACC, 2005)

Imposing Symmetry in Least Squares Support Vector Machines Regression. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (CDC/ECC, 2005)

Subspace intersection identification of Hammerstein-Wiener systems. - Ivan Goethals; Kristiaan Pelckmans; Luc Hoegaerts; Johan A. K. Suykens; Bart De Moor (CDC/ECC, 2005)

On Model Complexity Control in Identification of Hammerstein Systems. - Kristiaan Pelckmans; Johan A. K. Suykens; Ivan Goethals; Bart De Moor (CDC/ECC, 2005)

Interpolation based robust MPC with exact constraint handling. - J. Anthony Rossiter; Yihang Ding; Bert Pluymers; Johan A. K. Suykens; Bart De Moor (CDC/ECC, 2005)

M@CBETH: Optimizing Clinical Microarray Classification. - Nathalie Pochet; Frizo A. L. Janssens; Frank De Smet; Kathleen Marchal; Ignace Vergote; Johan A. K. Suykens; Bart De Moor (CSB Workshops, 2005)

Componentwise Support Vector Machines for Structure Detection. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (ICANN (2), 2005)

Extending kernel principal component analysis to general underlying loss functions. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2005)

Maximal variation and missing values for componentwise support vector machines. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor; Jos De Brabanter (IJCNN, 2005)

Spatiotemporal pattern formation in the ACE16k CNN chip. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (ISCAS (6), 2005)

Load Forecasting Using Fixed-Size Least Squares Support Vector Machines. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (IWANN, 2005)

Identification of MIMO Hammerstein models using least squares support vector machines. - Ivan Goethals; Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (Autom., 2005)

Constrained linear MPC with time-varying terminal cost using convex combinations. - Bert Pluymers; L. Roobrouck; J. Buijs; Johan A. K. Suykens; Bart De Moor (Autom., 2005)

M@CBETH: a microarray classification benchmarking tool. - Nathalie Pochet; Frizo A. L. Janssens; Frank De Smet; Kathleen Marchal; Johan A. K. Suykens; Bart De Moor (Bioinform., 2005)

Componentwise Least Squares Support Vector Machines - Kristiaan Pelckmans; Ivan Goethals; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (CoRR, 2005)

Subset based least squares subspace regression in RKHS. - Luc Hoegaerts; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (Neurocomputing, 2005)

The differogram: Non-parametric noise variance estimation and its use for model selection. - Kristiaan Pelckmans; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (Neurocomputing, 2005)

Building sparse representations and structure determination on LS-SVM substrates. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (Neurocomputing, 2005)

Handling missing values in support vector machine classifiers. - Kristiaan Pelckmans; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (Neural Networks, 2005)

Primal-Dual Monotone Kernel Regression. - Kristiaan Pelckmans; Marcelo Espinoza; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (Neural Process. Lett., 2005)

Min-max feedback MPC using a time-varying terminal constraint set and comments on "Efficient robust constrained model predictive control with a time-varying terminal constraint set". - Bert Pluymers; Johan A. K. Suykens; Bart De Moor (Syst. Control. Lett., 2005)

Kernel based partially linear models and nonlinear identification. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Autom. Control., 2005)

Subspace identification of Hammerstein systems using least squares support vector machines. - Ivan Goethals; Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Autom. Control., 2005)

Robust synthesis of constrained linear state feedback using LMIs and polyhedral invariant sets. - Bert Pluymers; Mayuresh V. Kothare; Johan A. K. Suykens; Bart De Moor (ACC, 2006)

A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2006)

Multi-scroll and hypercube attractors from Josephson junctions. - Müstak E. Yalçin; Johan A. K. Suykens; Joos Vandewalle (ISCAS, 2006)

Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting. - Marcelo Espinoza; Johan A. K. Suykens; Bart De Moor (Comput. Manag. Sci., 2006)

A process model to develop an internal rating system: Sovereign credit ratings. - Tony Van Gestel; Bart Baesens; Peter Van Dijcke; Joao Garcia; Johan A. K. Suykens; Jan Vanthienen (Decis. Support Syst., 2006)

Bayesian kernel based classification for financial distress detection. - Tony Van Gestel; Bart Baesens; Johan A. K. Suykens; Dirk Van den Poel; Dirk-Emma Baestaens; Marleen Willekens (Eur. J. Oper. Res., 2006)

Spatiotemporal Pattern Formation on the ACE16K CNN Chip. - Müstak E. Yalçin; Johan A. K. Suykens (Int. J. Bifurc. Chaos, 2006)

Learning of spatiotemporal behaviour in cellular neural networks. - Samuel Xavier de Souza; Johan A. K. Suykens; Joos Vandewalle (Int. J. Circuit Theory Appl., 2006)

Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (Mach. Learn., 2006)

Convex optimization for the design of learning machines. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (ESANN, 2007)

Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression. - Ben Van Calster; Jan Luts; Johan A. K. Suykens; George Condous; Tom Bourne; Dirk Timmerman; Sabine Van Huffel (ICANN (2), 2007)

State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition. - Joos Vandewalle; Johan A. K. Suykens; Bart De Moor; Amaury Lendasse (ICASSP (4), 2007)

ICA through an LS-SVM based Kernel CCA Measure for Independence. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2007)

Multi-class kernel logistic regression: a fixed-size implementation. - Peter Karsmakers; Kristiaan Pelckmans; Johan A. K. Suykens (IJCNN, 2007)

Variable selection by rank-one updates for least squares support vector machines. - Fabian Ojeda; Johan A. K. Suykens; Bart De Moor (IJCNN, 2007)

Fixed-size kernel logistic regression for phoneme classification. - Peter Karsmakers; Kristiaan Pelckmans; Johan A. K. Suykens; Hugo Van hamme (INTERSPEECH, 2007)

Transductive Rademacher Complexities for Learning Over a Graph. - Kristiaan Pelckmans; Johan A. K. Suykens (MLG, 2007)

A Risk Minimization Principle for a Class of Parzen Estimators. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (NIPS, 2007)

A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. - Jan Luts; Arend Heerschap; Johan A. K. Suykens; Sabine Van Huffel (Artif. Intell. Medicine, 2007)

Support and Quantile Tubes - Kristiaan Pelckmans; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (CoRR, 2007)

Partial Synchronization in oscillator Arrays with Asymmetric Coupling. - Dániel Hillier; Serkan Günel; Johan A. K. Suykens; Joos Vandewalle (Int. J. Bifurc. Chaos, 2007)

Margin based Transductive Graph Cuts using Linear Programming. - Kristiaan Pelckmans; John Shawe-Taylor; Johan A. K. Suykens; Bart De Moor (AISTATS, 2007)

Efficiently updating and tracking the dominant kernel principal components. - Luc Hoegaerts; Lieven De Lathauwer; Ivan Goethals; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (Neural Networks, 2007)

Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis. - Chuan Lu; Andy Devos; Johan A. K. Suykens; Carles Arús; Sabine Van Huffel (IEEE Trans. Inf. Technol. Biomed., 2007)

A Convex Approach to Validation-Based Learning of the Regularization Constant. - Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Neural Networks, 2007)

Application of the proximal center decomposition method to distributed model predictive control. - Ion Necoara; Minh Dang Doan; Johan A. K. Suykens (CDC, 2008)

A proximal center-based decomposition method for multi-agent convex optimization. - Ion Necoara; Johan A. K. Suykens (CDC, 2008)

Survival SVM: a practical scalable algorithm. - Vanya Van Belle; Kristiaan Pelckmans; Johan A. K. Suykens; Sabine Van Huffel (ESANN, 2008)

Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. - Marco Signoretto; Kristiaan Pelckmans; Johan A. K. Suykens (ICANN (1), 2008)

Sparse kernel models for spectral clustering using the incomplete Cholesky decomposition. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2008)

An Online Algorithm for Learning a Labeling of a Graph. - Kristiaan Pelckmans; Johan A. K. Suykens (MLG@ICML, 2008)

Robust triple mode MPC. - Lars Imsland; J. Anthony Rossiter; Bert Pluymers; Johan A. K. Suykens (Int. J. Control, 2008)

A regularized kernel CCA contrast function for ICA. - Carlos Alzate; Johan A. K. Suykens (Neural Networks, 2008)

Low rank updated LS-SVM classifiers for fast variable selection. - Fabian Ojeda; Johan A. K. Suykens; Bart De Moor (Neural Networks, 2008)

Application of a Smoothing Technique to Decomposition in Convex Optimization. - Ion Necoara; Johan A. K. Suykens (IEEE Trans. Autom. Control., 2008)

Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function. - Carlos Alzate; Johan A. K. Suykens (IEEE Trans. Neural Networks, 2008)

Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point. - Johan A. K. Suykens (IEEE Trans. Neural Networks, 2008)

Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. - Jan Luts; Johan A. K. Suykens; Sabine Van Huffel; Teresa Laudadio; Sofie Van Cauter; Uwe Himmelreich; Enrique Molla; Jose Piquer; M. Carmen Martínez-Bisbal; Bernardo Celda (CBMS, 2009)

Robustness analysis for Least Squares kernel based regression: an optimization approach. - Tillmann Falck; Johan A. K. Suykens; Bart De Moor (CDC, 2009)

Distributed nonlinear optimal control using sequential convex programming and smoothing techniques. - Ion Necoara; Carlo Savorgnan; Dinh Quoc Tran; Johan A. K. Suykens; Moritz Diehl (CDC, 2009)

Transductively Learning from Positive Examples Only. - Kristiaan Pelckmans; Johan A. K. Suykens (ESANN, 2009)

A dual interior-point distributed algorithm for large-scale data networks optimization. - Ion Necoara; Johan A. K. Suykens (ECC, 2009)

An improved dual decomposition approach to DSL dynamic spectrum management. - Paschalis Tsiaflakis; Ion Necoara; Johan A. K. Suykens; Marc Moonen (EUSIPCO, 2009)

Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering. - Carlos Alzate; Marcelo Espinoza; Bart De Moor; Johan A. K. Suykens (ICANN (2), 2009)

MINLIP: Efficient Learning of Transformation Models. - Vanya Van Belle; Kristiaan Pelckmans; Johan A. K. Suykens; Sabine Van Huffel (ICANN (1), 2009)

Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. - Kris De Brabanter; Kristiaan Pelckmans; Jos De Brabanter; Michiel Debruyne; Johan A. K. Suykens; Mia Hubert; Bart De Moor (ICANN (1), 2009)

Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling. - Adrien Combaz; Nikolay V. Manyakov; Nikolay Chumerin; Johan A. K. Suykens; Marc M. Van Hulle (ICMLA, 2009)

A regularized formulation for spectral clustering with pairwise constraints. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2009)

Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints. - Vanya Van Belle; Kristiaan Pelckmans; Johan A. K. Suykens; Sabine Van Huffel (IWANN (1), 2009)

P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface. - Nikolay Chumerin; Nikolay V. Manyakov; Adrien Combaz; Johan A. K. Suykens; Refet Firat Yazicioglu; Tom Torfs; Patrick Merken; Herc P. Neves; Chris Van Hoof; Marc M. Van Hulle (KI, 2009)

Least conservative support and tolerance tubes. - Kristiaan Pelckmans; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Inf. Theory, 2009)

Linear parametric noise models for Least Squares Support Vector Machines. - Tillmann Falck; Johan A. K. Suykens; Bart De Moor (CDC, 2010)

Nuclear norm regularization for overparametrized Hammerstein systems. - Tillmann Falck; Johan A. K. Suykens; Johan Schoukens; Bart De Moor (CDC, 2010)

Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization. - Ion Necoara; Ioan Dumitrache; Johan A. K. Suykens (CDC, 2010)

Improved non-parametric sparse recovery with data matched penalties. - Marco Signoretto; Kristiaan Pelckmans; Lieven De Lathauwer; Johan A. K. Suykens (CIP, 2010)

Highly sparse kernel spectral clustering with predictive out-of-sample extensions. - Carlos Alzate; Johan A. K. Suykens (ESANN, 2010)

On the use of a clinical kernel in survival analysis. - Vanya Van Belle; Kristiaan Pelckmans; Johan A. K. Suykens; Sabine Van Huffel (ESANN, 2010)

Efficient adaptive filtering for smooth linear FIR models. - Kristiaan Pelckmans; Toon van Waterschoot; Johan A. K. Suykens (EUSIPCO, 2010)

Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. - Marco Signoretto; Lieven De Lathauwer; Johan A. K. Suykens (ICANN (2), 2010)

Polynomial componentwise LS-SVM: Fast variable selection using low rank updates. - Fabian Ojeda; Tillmann Falck; Bart De Moor; Johan A. K. Suykens (IJCNN, 2010)

Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. - Fabian Ojeda; Marco Signoretto; Raf Van de Plas; Etienne Waelkens; Bart De Moor; Johan A. K. Suykens (PRIB, 2010)

L2-norm multiple kernel learning and its application to biomedical data fusion. - Shi Yu; Tillmann Falck; Anneleen Daemen; Léon-Charles Tranchevent; Johan A. K. Suykens; Bart De Moor; Yves Moreau (BMC Bioinform., 2010)

Optimized fixed-size kernel models for large data sets. - Kris De Brabanter; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (Comput. Stat. Data Anal., 2010)

Robustness of reweighted Least Squares Kernel Based Regression. - Michiel Debruyne; Andreas Christmann; Mia Hubert; Johan A. K. Suykens (J. Multivar. Anal., 2010)

Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA. - Carlos Alzate; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Coupled Simulated Annealing. - Samuel Xavier de Souza; Johan A. K. Suykens; Joos Vandewalle; Désiré Bollé (IEEE Trans. Syst. Man Cybern. Part B, 2010)

Improved dual decomposition based optimization for DSL dynamic spectrum management. - Paschalis Tsiaflakis; Ion Necoara; Johan A. K. Suykens; Marc Moonen (IEEE Trans. Signal Process., 2010)

Sparse LS-SVMs with L0 - norm minimization. - Jorge López Lázaro; Kris De Brabanter; José R. Dorronsoro; Johan A. K. Suykens (ESANN, 2011)

Symbolic computing of LS-SVM based models. - Siamak Mehrkanoon; Li Jiang; Carlos Alzate; Johan A. K. Suykens (ESANN, 2011)

Automatic Seizure Detection Incorporating Structural Information. - Borbála Hunyadi; Maarten De Vos; Marco Signoretto; Johan A. K. Suykens; Wim Van Paesschen; Sabine Van Huffel (ICANN (1), 2011)

Out-of-sample eigenvectors in kernel spectral clustering. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2011)

Modularity-based model selection for kernel spectral clustering. - Rocco Langone; Carlos Alzate; Johan A. K. Suykens (IJCNN, 2011)

Support vector methods for survival analysis: a comparison between ranking and regression approaches. - Vanya Van Belle; Kristiaan Pelckmans; Sabine Van Huffel; Johan A. K. Suykens (Artif. Intell. Medicine, 2011)

Improved performance on high-dimensional survival data by application of Survival-SVM. - Vanya Van Belle; Kristiaan Pelckmans; Sabine Van Huffel; Johan A. K. Suykens (Bioinform., 2011)

Optimized data fusion for K-means Laplacian clustering. - Shi Yu; Xinhai Liu; Léon-Charles Tranchevent; Wolfgang Glänzel; Johan A. K. Suykens; Bart De Moor; Yves Moreau (Bioinform., 2011)

On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation. - Geert J. Postma; Jan Luts; Albert J. Idema; Margarida Julià-Sapé; Àngel Moreno-Torres; Witek Gajewicz; Johan A. K. Suykens; Arend Heerschap; Sabine Van Huffel; Lutgarde M. C. Buydens (Comput. Biol. Medicine, 2011)

Sparse kernel spectral clustering models for large-scale data analysis. - Carlos Alzate; Johan A. K. Suykens (Neurocomputing, 2011)

Learning Transformation Models for Ranking and Survival Analysis. - Vanya Van Belle; Kristiaan Pelckmans; Johan A. K. Suykens; Sabine Van Huffel (J. Mach. Learn. Res., 2011)

Kernel Regression in the Presence of Correlated Errors. - Kris De Brabanter; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (J. Mach. Learn. Res., 2011)

Sparse conjugate directions pursuit with application to fixed-size kernel models. - Peter Karsmakers; Kristiaan Pelckmans; Kris De Brabanter; Hugo Van hamme; Johan A. K. Suykens (Mach. Learn., 2011)

A kernel-based framework to tensorial data analysis. - Marco Signoretto; Lieven De Lathauwer; Johan A. K. Suykens (Neural Networks, 2011)

First and Second Order SMO Algorithms for LS-SVM Classifiers. - Jorge López Lázaro; Johan A. K. Suykens (Neural Process. Lett., 2011)

Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data. - Marco Signoretto; Raf Van de Plas; Bart De Moor; Johan A. K. Suykens (IEEE Signal Process. Lett., 2011)

Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. - Kris De Brabanter; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Neural Networks, 2011)

Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis. - Carolina Varon; Dries Testelmans; Bertien Buyse; Johan A. K. Suykens; Sabine Van Huffel (EMBC, 2012)

Interval coded scoring systems for survival analysis. - Vanya Van Belle; Sabine Van Huffel; Johan A. K. Suykens; Stephen P. Boyd (ESANN, 2012)

Joint Regression and Linear Combination of Time Series for Optimal Prediction. - Dries Geebelen; Kim Batselier; Philippe Dreesen; Marco Signoretto; Johan A. K. Suykens; Bart De Moor; Joos Vandewalle (ESANN, 2012)

A semi-supervised formulation to binary kernel spectral clustering. - Carlos Alzate; Johan A. K. Suykens (IJCNN, 2012)

Robustness of kernel based regression: Influence and weight functions. - Kris De Brabanter; Jos De Brabanter; Johan A. K. Suykens; Joos Vandewalle; Bart De Moor (IJCNN, 2012)

Kernel spectral clustering for community detection in complex networks. - Rocco Langone; Carlos Alzate; Johan A. K. Suykens (IJCNN, 2012)

LS-SVM approximate solution to linear time varying descriptor systems. - Siamak Mehrkanoon; Johan A. K. Suykens (Autom., 2012)

A mixed effects least squares support vector machine model for classification of longitudinal data. - Jan Luts; Geert Molenberghs; Geert Verbeke; Sabine Van Huffel; Johan A. K. Suykens (Comput. Stat. Data Anal., 2012)

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface. - Adrien Combaz; Nikolay Chumerin; Nikolay V. Manyakov; Arne Robben; Johan A. K. Suykens; Marc M. Van Hulle (Neurocomputing, 2012)

Hierarchical kernel spectral clustering. - Carlos Alzate; Johan A. K. Suykens (Neural Networks, 2012)

Optimized Data Fusion for Kernel k-Means Clustering. - Shi Yu; Léon-Charles Tranchevent; Xinhai Liu; Wolfgang Glänzel; Johan A. K. Suykens; Bart De Moor; Yves Moreau (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Confidence bands for least squares support vector machine classifiers: A regression approach. - Kris De Brabanter; Peter Karsmakers; Jos De Brabanter; Johan A. K. Suykens; Bart De Moor (Pattern Recognit., 2012)

Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration. - Devy Widjaja; Carolina Varon; Alexander Dorado; Johan A. K. Suykens; Sabine Van Huffel (IEEE Trans. Biomed. Eng., 2012)

Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation. - Dries Geebelen; Johan A. K. Suykens; Joos Vandewalle (IEEE Trans. Neural Networks Learn. Syst., 2012)

Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines. - Siamak Mehrkanoon; Tillmann Falck; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2012)

Classification of Multichannel Signals With Cumulant-Based Kernels. - Marco Signoretto; Emanuele Olivetti; Lieven De Lathauwer; Johan A. K. Suykens (IEEE Trans. Signal Process., 2012)

Self-tuned kernel spectral clustering for large scale networks. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (IEEE BigData, 2013)

Supervised Novelty Detection. - Vilen Jumutc; Johan A. K. Suykens (CIDM, 2013)

Kernel spectral clustering for predicting maintenance of industrial machines. - Rocco Langone; Carlos Alzate; Bart De Ketelaere; Johan A. K. Suykens (CIDM, 2013)

DynOpt: Incorporating dynamics into mean-variance portfolio optimization. - Marco Signoretto; Johan A. K. Suykens (CIFEr, 2013)

Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines. - Raghvendra Mall; Johan A. K. Suykens; Mohammed El Anbari; Halima Bensmail (COMAD, 2013)

Sleep apnea classification using least-squares support vector machines on single lead ECG. - Carolina Varon; Dries Testelmans; Bertien Buyse; Johan A. K. Suykens; Sabine Van Huffel (EMBC, 2013)

Fixed-size Pegasos for hinge and pinball loss SVM. - Vilen Jumutc; Xiaolin Huang; Johan A. K. Suykens (IJCNN, 2013)

Soft kernel spectral clustering. - Rocco Langone; Raghvendra Mall; Johan A. K. Suykens (IJCNN, 2013)

Non-parallel semi-supervised classification based on kernel spectral clustering. - Siamak Mehrkanoon; Johan A. K. Suykens (IJCNN, 2013)

Kernel spectral clustering for dynamic data using multiple kernel learning. - Diego Hernán Peluffo-Ordóñez; Sergio García-Vega; Rocco Langone; Johan A. K. Suykens; Germán Castellanos-Domínguez (IJCNN, 2013)

Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data. - Raghvendra Mall; Johan A. K. Suykens (PAKDD (1), 2013)

Weighted Coordinate-Wise Pegasos. - Vilen Jumutc; Johan A. K. Suykens (PReMI, 2013)

Highly Sparse Reductions to Kernel Spectral Clustering. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (PReMI, 2013)

The skweezee system: enabling the design and the programming of squeeze interactions. - Karen Vanderloock; Vero Vanden Abeele; Johan A. K. Suykens; Luc Geurts (UIST, 2013)

Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties. - Marco Signoretto; Lieven De Lathauwer; Johan A. K. Suykens (CoRR, 2013)

Kernel Spectral Clustering for Big Data Networks. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (Entropy, 2013)

Load forecasting using a multivariate meta-learning system. - Marin Matijas; Johan A. K. Suykens; Slavko Krajcar (Expert Syst. Appl., 2013)

Risk group detection and survival function estimation for interval coded survival methods. - Vanya Van Belle; Patrick Neven; Vernon Harvey; Sabine Van Huffel; Johan A. K. Suykens; Stephen P. Boyd (Neurocomputing, 2013)

Support vector machines with piecewise linear feature mapping. - Xiaolin Huang; Siamak Mehrkanoon; Johan A. K. Suykens (Neurocomputing, 2013)

FURS: Fast and Unique Representative Subset selection retaining large-scale community structure. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (Soc. Netw. Anal. Min., 2013)

Stability of Coupled Local Minimizers Within the Lagrange Programming Network Framework. - Xuyang Lou; Johan A. K. Suykens (IEEE Trans. Circuits Syst. I Regul. Pap., 2013)

Hinging Hyperplanes for Time-Series Segmentation. - Xiaolin Huang; Marin Matijas; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2013)

Representative subsets for big data learning using k-NN graphs. - Raghvendra Mall; Vilen Jumutc; Rocco Langone; Johan A. K. Suykens (IEEE BigData, 2014)

High level high performance computing for multitask learning of time-varying models. - Marco Signoretto; Emanuele Frandi; Zahra Karevan; Johan A. K. Suykens (CIBD, 2014)

New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering. - Vilen Jumutc; Johan A. K. Suykens (CIDM, 2014)

Alarm prediction in industrial machines using autoregressive LS-SVM models. - Rocco Langone; Carlos Alzate; Abdellatif Bey-Temsamani; Johan A. K. Suykens (CIDM, 2014)

Clustering data over time using kernel spectral clustering with memory. - Rocco Langone; Raghvendra Mall; Johan A. K. Suykens (CIDM, 2014)

Agglomerative hierarchical kernel spectral data clustering. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (CIDM, 2014)

Gene interaction networks boost genetic algorithm performance in biomarker discovery. - Charalampos N. Moschopoulos; Dusan Popovic; Rocco Langone; Johan A. K. Suykens; Bart De Moor; Yves Moreau (MCDM, 2014)

Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning. - Vilen Jumutc; Johan A. K. Suykens (ESANN, 2014)

Agglomerative hierarchical kernel spectral clustering for large scale networks. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (ESANN, 2014)

Optimal Data Projection for Kernel Spectral Clustering. - Diego Hernán Peluffo-Ordóñez; Carlos Alzate; Johan A. K. Suykens; Germán Castellanos-Domínguez (ESANN, 2014)

SVD truncation schemes for fixed-size kernel models. - Ricardo Castro-Garcia; Siamak Mehrkanoon; Anna Marconato; Johan Schoukens; Johan A. K. Suykens (IJCNN, 2014)

Optimal reduced sets for sparse kernel spectral clustering. - Raghvendra Mall; Siamak Mehrkanoon; Rocco Langone; Johan A. K. Suykens (IJCNN, 2014)

Large scale semi-supervised learning using KSC based model. - Siamak Mehrkanoon; Johan A. K. Suykens (IJCNN, 2014)

Reweighted l 2-Regularized Dual Averaging Approach for Highly Sparse Stochastic Learning. - Vilen Jumutc; Johan A. K. Suykens (ISNN, 2014)

Quantile regression with ℓ 1 - regularization and Gaussian kernels. - Lei Shi 010; Xiaolin Huang; Zheng Tian; Johan A. K. Suykens (Adv. Comput. Math., 2014)

Predicting breast cancer using an expression values weighted clinical classifier. - Minta Thomas; Kris De Brabanter; Johan A. K. Suykens; Bart De Moor (BMC Bioinform., 2014)

Parameter estimation of delay differential equations: An integration-free LS-SVM approach. - Siamak Mehrkanoon; Saeid Mehrkanoon; Johan A. K. Suykens (Commun. Nonlinear Sci. Numer. Simul., 2014)

Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization. - Andreas Argyriou; Marco Signoretto; Johan A. K. Suykens (CoRR, 2014)

Fast Prediction with SVM Models Containing RBF Kernels. - Marc Claesen; Frank De Smet; Johan A. K. Suykens; Bart De Moor (CoRR, 2014)

Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning. - Emanuele Frandi; Ricardo Ñanculef; Johan A. K. Suykens (CoRR, 2014)

Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (CoRR, 2014)

Asymmetric v-tube support vector regression. - Xiaolin Huang; Lei Shi 010; Kristiaan Pelckmans; Johan A. K. Suykens (Comput. Stat. Data Anal., 2014)

Asymmetric least squares support vector machine classifiers. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (Comput. Stat. Data Anal., 2014)

Incremental kernel spectral clustering for online learning of non-stationary data. - Rocco Langone; Oscar Mauricio Agudelo; Bart De Moor; Johan A. K. Suykens (Neurocomputing, 2014)

Non-parallel support vector classifiers with different loss functions. - Siamak Mehrkanoon; Xiaolin Huang; Johan A. K. Suykens (Neurocomputing, 2014)

QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset. - Dries Geebelen; Kristof Geebelen; Eddy Truyen; Sam Michiels; Johan A. K. Suykens; Joos Vandewalle; Wouter Joosen (Inf. Sci., 2014)

EnsembleSVM: a library for ensemble learning using support vector machines. - Marc Claesen; Frank De Smet; Johan A. K. Suykens; Bart De Moor (J. Mach. Learn. Res., 2014)

Ramp loss linear programming support vector machine. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (J. Mach. Learn. Res., 2014)

Learning with tensors: a framework based on convex optimization and spectral regularization. - Marco Signoretto; Dinh Quoc Tran; Lieven De Lathauwer; Johan A. K. Suykens (Mach. Learn., 2014)

Support Vector Machine Classifier With Pinball Loss. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Multi-Class Supervised Novelty Detection. - Vilen Jumutc; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Hybrid Coupled Local Minimizers. - Xuyang Lou; Johan A. K. Suykens (IEEE Trans. Circuits Syst. I Regul. Pap., 2014)

Improved Initialization for Nonlinear State-Space Modeling. - Anna Marconato; Jonas Sjöberg; Johan A. K. Suykens; Johan Schoukens (IEEE Trans. Instrum. Meas., 2014)

KSC-net: Community Detection for Big Data Networks. - Raghvendra Mall; Johan A. K. Suykens (Big Data - Algorithms, Analytics, and Applications, 2015)

Regularized and sparse stochastic k-means for distributed large-scale clustering. - Vilen Jumutc; Rocco Langone; Johan A. K. Suykens (IEEE BigData, 2015)

Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. - Ricardo Castro-Garcia; Koen Tiels; Johan Schoukens; Johan A. K. Suykens (CDC, 2015)

Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering. - Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (ESANN, 2015)

A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification. - Emanuele Frandi; Ricardo Ñanculef; Johan A. K. Suykens (IJCNN, 2015)

Black-box modeling for temperature prediction in weather forecasting. - Zahra Karevan; Siamak Mehrkanoon; Johan A. K. Suykens (IJCNN, 2015)

Kernel spectral document clustering using unsupervised precision-recall metrics. - Raghvendra Mall; Johan A. K. Suykens (IJCNN, 2015)

Hierarchical semi-supervised clustering using KSC based model. - Siamak Mehrkanoon; Oscar Mauricio Agudelo; Raghvendra Mall; Johan A. K. Suykens (IJCNN, 2015)

Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification. - Mandar Chandorkar; Raghvendra Mall; Oliver Lauwers; Johan A. K. Suykens; Bart De Moor (SSCI, 2015)

Pinball Loss Minimization for One-bit Compressive Sensing. - Xiaolin Huang; Lei Shi 010; Ming Yan 006; Johan A. K. Suykens (CoRR, 2015)

Kernel Spectral Clustering and applications. - Rocco Langone; Raghvendra Mall; Carlos Alzate; Johan A. K. Suykens (CoRR, 2015)

Higher order Matching Pursuit for Low Rank Tensor Learning. - Yuning Yang; Siamak Mehrkanoon; Johan A. K. Suykens (CoRR, 2015)

LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. - Rocco Langone; Carlos Alzate; Bart De Ketelaere; Jonas Vlasselaer; Wannes Meert; Johan A. K. Suykens (Eng. Appl. Artif. Intell., 2015)

A robust ensemble approach to learn from positive and unlabeled data using SVM base models. - Marc Claesen; Frank De Smet; Johan A. K. Suykens; Bart De Moor (Neurocomputing, 2015)

Sequential minimal optimization for SVM with pinball loss. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (Neurocomputing, 2015)

Learning solutions to partial differential equations using LS-SVM. - Siamak Mehrkanoon; Johan A. K. Suykens (Neurocomputing, 2015)

Learning with the maximum correntropy criterion induced losses for regression. - Yunlong Feng; Xiaolin Huang; Lei Shi 010; Yuning Yang; Johan A. K. Suykens (J. Mach. Learn. Res., 2015)

Incremental multi-class semi-supervised clustering regularized by Kalman filtering. - Siamak Mehrkanoon; Oscar Mauricio Agudelo; Johan A. K. Suykens (Neural Networks, 2015)

Identifying intervals for hierarchical clustering using the Gershgorin circle theorem. - Raghvendra Mall; Siamak Mehrkanoon; Johan A. K. Suykens (Pattern Recognit. Lett., 2015)

Two-level ℓ1 minimization for compressed sensing. - Xiaolin Huang; Yipeng Liu 001; Lei Shi 010; Sabine Van Huffel; Johan A. K. Suykens (Signal Process., 2015)

Signal recovery for jointly sparse vectors with different sensing matrices. - Li Li 013; Xiaolin Huang; Johan A. K. Suykens (Signal Process., 2015)

A Rank-One Tensor Updating Algorithm for Tensor Completion. - Yuning Yang; Yunlong Feng; Johan A. K. Suykens (IEEE Signal Process. Lett., 2015)

Very Sparse LSSVM Reductions for Large-Scale Data. - Raghvendra Mall; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2015)

Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering. - Siamak Mehrkanoon; Carlos Alzate; Raghvendra Mall; Rocco Langone; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2015)

Noise Level Estimation for Model Selection in Kernel PCA Denoising. - Carolina Varon; Carlos Alzate; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2015)

Kernel Methods. - Marco Signoretto; Johan A. K. Suykens (Handbook of Computational Intelligence, 2015)

Efficient multiple scale kernel classifiers. - Rocco Langone; Johan A. K. Suykens (IEEE BigData, 2016)

Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso. - Marcelo Aliquintuy; Emanuele Frandi; Ricardo Ñanculef; Johan A. K. Suykens (CIARP, 2016)

Clustering from two data sources using a kernel-based approach with weight coupling. - Lynn Houthuys; Rocco Langone; Johan A. K. Suykens (ESANN, 2016)

Spatio-temporal feature selection for black-box weather forecasting. - Zahra Karevan; Johan A. K. Suykens (ESANN, 2016)

Fast in-memory spectral clustering using a fixed-size approach. - Rocco Langone; Raghvendra Mall; Vilen Jumutc; Johan A. K. Suykens (ESANN, 2016)

Hammerstein system identification using LS-SVM and steady state time response. - Ricardo Castro-Garcia; Oscar Mauricio Agudelo; Koen Tiels; Johan A. K. Suykens (ECC, 2016)

Clustering-based feature selection for black-box weather temperature prediction. - Zahra Karevan; Johan A. K. Suykens (IJCNN, 2016)

Denoised Kernel Spectral data Clustering. - Raghvendra Mall; Halima Bensmail; Rocco Langone; Carolina Varon; Johan A. K. Suykens (IJCNN, 2016)

Multi-label semi-supervised learning using regularized kernel spectral clustering. - Siamak Mehrkanoon; Johan A. K. Suykens (IJCNN, 2016)

Wiener System Identification using Best Linear Approximation within the LS-SVM framework. - Ricardo Castro-Garcia; Johan A. K. Suykens (LA-CCI, 2016)

Scalable Semi-supervised kernel spectral learning using random Fourier features. - Siamak Mehrkanoon; Johan A. K. Suykens (SSCI, 2016)

The effect of imposing 'fractional abundance constraints' onto the multilayer perceptron for sub-pixel land cover classification. - Stien Heremans; Johan A. K. Suykens; Jos Van Orshoven (Int. J. Appl. Earth Obs. Geoinformation, 2016)

Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (CoRR, 2016)

Magnetic eigenmaps for community detection in directed networks. - Michaël Fanuel; Carlos M. Alaíz; Johan A. K. Suykens (CoRR, 2016)

Magnetic Eigenmaps for Visualization of Directed Networks. - Michaël Fanuel; Ángela Fernández 001; Carlos M. Alaíz; Johan A. K. Suykens (CoRR, 2016)

Estimating the unknown time delay in chemical processes. - Siamak Mehrkanoon; Yuri A. W. Shardt; Johan A. K. Suykens; Steven X. Ding (Eng. Appl. Artif. Intell., 2016)

Entropy-Based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis. - Rocco Langone; Marc Van Barel; Johan A. K. Suykens (Entropy, 2016)

Reweighted stochastic learning. - Vilen Jumutc; Johan A. K. Suykens (Neurocomputing, 2016)

Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee. - Emanuele Frandi; Ricardo Ñanculef; Stefano Lodi; Claudio Sartori 001; Johan A. K. Suykens (Mach. Learn., 2016)

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery. - Yunlong Feng; Shao-Gao Lv; Hanyuan Hang; Johan A. K. Suykens (Neural Comput., 2016)

Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses. - Yunlong Feng; Yuning Yang; Xiaolin Huang; Siamak Mehrkanoon; Johan A. K. Suykens (Neural Comput., 2016)

Learning Theory Estimates with Observations from General Stationary Stochastic Processes. - Hanyuan Hang; Yunlong Feng; Ingo Steinwart; Johan A. K. Suykens (Neural Comput., 2016)

Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines. - Xiangming Xi; Xiaolin Huang; Johan A. K. Suykens; Shuning Wang (Neural Process. Lett., 2016)

Efficient evolutionary spectral clustering. - Rocco Langone; Marc Van Barel; Johan A. K. Suykens (Pattern Recognit. Lett., 2016)

Rank-1 Tensor Properties with Applications to a Class of Tensor Optimization Problems. - Yuning Yang; Yunlong Feng; Xiaolin Huang; Johan A. K. Suykens (SIAM J. Optim., 2016)

Robust Gradient Learning With Applications. - Yunlong Feng; Yuning Yang; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2016)

Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator. - Yuning Yang; Yunlong Feng; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2016)

On the identification of Wiener systems with polynomial nonlinearity. - Giulio Bottegal; Ricardo Castro-Garcia; Johan A. K. Suykens (CDC, 2017)

Moving Least Squares Support Vector Machines for weather temperature prediction. - Zahra Karevan; Yunlong Feng; Johan A. K. Suykens (ESANN, 2017)

Scalable Hybrid Deep Neural Kernel Networks. - Siamak Mehrkanoon; Andreas Zell; Johan A. K. Suykens (ESANN, 2017)

A primal-dual line search method and applications in image processing. - Pantelis Sopasakis; Andreas Themelis; Johan A. K. Suykens; Panagiotis Patrinos (EUSIPCO, 2017)

Probabilistic matrix factorization from quantized measurements. - Giulio Bottegal; Johan A. K. Suykens (IJCNN, 2017)

Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting. - Lynn Houthuys; Zahra Karevan; Johan A. K. Suykens (IJCNN, 2017)

MIMO hammerstein system identification using LS-SVM and steady state time response. - Ricardo Castro-Garcia; Oscar Mauricio Agudelo; Johan A. K. Suykens (SSCI, 2017)

Unpaired multi-view kernel spectral clustering. - Lynn Houthuys; Johan A. K. Suykens (SSCI, 2017)

Fast kernel spectral clustering. - Rocco Langone; Johan A. K. Suykens (Neurocomputing, 2017)

Supervised aggregated feature learning for multiple instance classification. - Rocco Langone; Johan A. K. Suykens (Inf. Sci., 2017)

Deep Restricted Kernel Machines Using Conjugate Feature Duality. - Johan A. K. Suykens (Neural Comput., 2017)

Editorial: A Successful Year and Looking Forward to 2017 and Beyond. - Haibo He; Robert Haas; Jun Fu; Barbara Hammer; Daniel W. C. Ho; Fakhri Karray; Dhireesha Kudithipudi; José Antonio Lozano 001; Teresa Bernarda Ludermir; Jacek Mandziuk; Stefano Melacci; Antonio Paiva; et al. (IEEE Trans. Neural Networks Learn. Syst., 2017)

Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. - Xiaolin Huang; Lei Shi 010; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2017)

Solving lp-norm regularization with tensor kernels. - Saverio Salzo; Lorenzo Rosasco; Johan A. K. Suykens (AISTATS, 2018)

Valuing Knowledge, Information and Agency in Multi-agent Reinforcement Learning: A Case Study in Smart Buildings. - Hussain Syed Kazmi; Johan A. K. Suykens; Johan Driesen (AAMAS, 2018)

Fast Adaptive Hinging Hyperplanes. - Qinghua Tao; Jun Xu 008; Johan A. K. Suykens; Shuning Wang (CDC, 2018)

Shallow and Deep Models for Domain Adaptation problems. - Siamak Mehrkanoon; Matthew B. Blaschko; Johan A. K. Suykens (ESANN, 2018)

Generative Kernel PCA. - Joachim Schreurs; Johan A. K. Suykens (ESANN, 2018)

Tensor Learning in Multi-view Kernel PCA. - Lynn Houthuys; Johan A. K. Suykens (ICANN (2), 2018)

Weighted Multi-view Deep Neural Networks for Weather Forecasting. - Zahra Karevan; Lynn Houthuys; Johan A. K. Suykens (ICANN (3), 2018)

Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. - Sundaravelpandian Singaravel; Johan A. K. Suykens; Philipp Geyer (Adv. Eng. Informatics, 2018)

A two-experiment approach to Wiener system identification. - Giulio Bottegal; Ricardo Castro-Garcia; Johan A. K. Suykens (Autom., 2018)

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2018)

Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. - Zahra Karevan; Johan A. K. Suykens (CoRR, 2018)

Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting. - Zahra Karevan; Johan A. K. Suykens (Entropy, 2018)

Correntropy Based Matrix Completion. - Yuning Yang; Yunlong Feng; Johan A. K. Suykens (Entropy, 2018)

Hammerstein system identification through best linear approximation inversion and regularisation. - Ricardo Castro-Garcia; Koen Tiels; Oscar Mauricio Agudelo; Johan A. K. Suykens (Int. J. Control, 2018)

Modified Frank-Wolfe algorithm for enhanced sparsity in support vector machine classifiers. - Carlos M. Alaíz; Johan A. K. Suykens (Neurocomputing, 2018)

Multi-View Least Squares Support Vector Machines Classification. - Lynn Houthuys; Rocco Langone; Johan A. K. Suykens (Neurocomputing, 2018)

Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms. - Xiaolin Huang; Lei Shi 010; Ming Yan 006; Johan A. K. Suykens (Neurocomputing, 2018)

Deep hybrid neural-kernel networks using random Fourier features. - Siamak Mehrkanoon; Johan A. K. Suykens (Neurocomputing, 2018)

Multi-View Kernel Spectral Clustering. - Lynn Houthuys; Rocco Langone; Johan A. K. Suykens (Inf. Fusion, 2018)

Kernel Density Estimation for Dynamical Systems. - Hanyuan Hang; Ingo Steinwart; Yunlong Feng; Johan A. K. Suykens (J. Mach. Learn. Res., 2018)

Indefinite kernel spectral learning. - Siamak Mehrkanoon; Xiaolin Huang; Johan A. K. Suykens (Pattern Recognit., 2018)

Optimal Quadrature-Sparsification for Integral Operator Approximation. - Bertrand Gauthier; Johan A. K. Suykens (SIAM J. Sci. Comput., 2018)

Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2018)

Parallelized Tensor Train Learning of Polynomial Classifiers. - Zhongming Chen; Kim Batselier; Johan A. K. Suykens; Ngai Wong 001 (IEEE Trans. Neural Networks Learn. Syst., 2018)

Classification With Truncated $ ell _{1}$ Distance Kernel. - Xiaolin Huang; Johan A. K. Suykens; Shuning Wang; Joachim Hornegger; Andreas K. Maier (IEEE Trans. Neural Networks Learn. Syst., 2018)

Regularized Semipaired Kernel CCA for Domain Adaptation. - Siamak Mehrkanoon; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2018)

Towards Deterministic Diverse Subset Sampling. - Joachim Schreurs; Michaël Fanuel; Johan A. K. Suykens (BNAIC/BENELEARN (Selected Papers), 2019)

Latent Space Exploration Using Generative Kernel PCA. - David Winant; Joachim Schreurs; Johan A. K. Suykens (BNAIC/BENELEARN (Selected Papers), 2019)

Axiomatic Kernels on Graphs for Support Vector Machines. - Marcin Orchel; Johan A. K. Suykens (ICANN (Workshop), 2019)

Deep convolutional learning for general early design stage prediction models. - Sundaravelpandian Singaravel; Johan A. K. Suykens; Philipp Geyer (Adv. Eng. Informatics, 2019)

Two-stage Best-scored Random Forest for Large-scale Regression. - Hanyuan Hang; Yingyi Chen; Johan A. K. Suykens (CoRR, 2019)

Generative Restricted Kernel Machines. - Arun Pandey; Joachim Schreurs; Johan A. K. Suykens (CoRR, 2019)

Robust classification of graph-based data. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (Data Min. Knowl. Discov., 2019)

Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. - Ricardo Castro-Garcia; Oscar Mauricio Agudelo; Johan A. K. Suykens (Int. J. Control, 2019)

Sparse Kernel Regression with Coefficient-based $ ell_q-$regularization. - Lei Shi 010; Xiaolin Huang; Yunlong Feng; Johan A. K. Suykens (J. Mach. Learn. Res., 2019)

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. - Fanghui Liu 001; Xiaolin Huang; Chen Gong 002; Jie Yang 002; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2019)

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Jie Yang 002; Johan A. K. Suykens (AAAI, 2020)

Learning from partially labeled data. - Siamak Mehrkanoon; Xiaolin Huang; Johan A. K. Suykens (ESANN, 2020)

Wasserstein Exponential Kernels. - Henri De Plaen; Michaël Fanuel; Johan A. K. Suykens (IJCNN, 2020)

Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. - Marcin Orchel; Johan A. K. Suykens (LOD (2), 2020)

Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality. - Arun Pandey; Joachim Schreurs; Johan A. K. Suykens (LOD (1), 2020)

A Theoretical Framework for Target Propagation. - Alexander Meulemans; Francesco S. Carzaniga; Johan A. K. Suykens; João Sacramento; Benjamin F. Grewe (NeurIPS, 2020)

Efficient hinging hyperplanes neural network and its application in nonlinear system identification. - Jun Xu 008; Qinghua Tao; Zhen Li 032; Xiangming Xi; Johan A. K. Suykens; Shuning Wang (Autom., 2020)

Generalizing Random Fourier Features via Generalized Measures. - Fanghui Liu 001; Xiaolin Huang; Yingyi Chen; Johan A. K. Suykens (CoRR, 2020)

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2020)

The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. - Hannes De Meulemeester; Joachim Schreurs; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (CoRR, 2020)

Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. - Joachim Schreurs; Michaël Fanuel; Johan A. K. Suykens (CoRR, 2020)

Outlier detection in non-elliptical data by kernel MRCD. - Joachim Schreurs; Iwein Vranckx; Bart De Ketelaere; Mia Hubert; Johan A. K. Suykens; Peter J. Rousseeuw (CoRR, 2020)

Kernel regression in high dimension: Refined analysis beyond double descent. - Fanghui Liu 001; Zhenyu Liao 001; Johan A. K. Suykens (CoRR, 2020)

Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (CoRR, 2020)

A Statistical Learning Approach to Modal Regression. - Yunlong Feng; Jun Fan; Johan A. K. Suykens (J. Mach. Learn. Res., 2020)

Transductive LSTM for time-series prediction: An application to weather forecasting. - Zahra Karevan; Johan A. K. Suykens (Neural Networks, 2020)

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. - Fanghui Liu 001; Xiaolin Huang; Lei Shi 010; Jie Yang 002; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2020)

Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. - Fanghui Liu 001; Xiaolin Huang; Yingyi Chen; Johan A. K. Suykens (AISTATS, 2021)

Kernel regression in high dimensions: Refined analysis beyond double descent. - Fanghui Liu 001; Zhenyu Liao 001; Johan A. K. Suykens (AISTATS, 2021)

Neural Network Training as an Optimal Control Problem : - An Augmented Lagrangian Approach -. - Brecht Evens; Puya Latafat; Andreas Themelis; Johan A. K. Suykens; Panagiotis Patrinos (CDC, 2021)

Boosting Co-Teaching With Compression Regularization for Label Noise. - Yingyi Chen; Xi Shen 001; Shell Xu Hu; Johan A. K. Suykens (CVPR Workshops, 2021)

Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. - Francesco Tonin; Arun Pandey; Panagiotis Patrinos; Johan A. K. Suykens (IJCNN, 2021)

Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines. - Marcin Orchel; Johan A. K. Suykens (LOD, 2021)

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. - Joachim Schreurs; Hannes De Meulemeester; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (LOD, 2021)

The Bures Metric for Generative Adversarial Networks. - Hannes De Meulemeester; Joachim Schreurs; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (ECML/PKDD (2), 2021)

Kernel Machines in Time (Invited Talk). - Johan A. K. Suykens (TIME, 2021)

Tensor Network Kalman Filtering for Large-Scale LS-SVMs. - Maximilian Lucassen; Johan A. K. Suykens; Kim Batselier (CoRR, 2021)

Learning with continuous piecewise linear decision trees. - Qinghua Tao; Zhen Li 032; Jun Xu 008; Na Xie; Shuning Wang; Johan A. K. Suykens (Expert Syst. Appl., 2021)

A novel neural grey system model with Bayesian regularization and its applications. - Xin Ma 004; Mei Xie; Johan A. K. Suykens (Neurocomputing, 2021)

Tensor-based restricted kernel machines for multi-view classification. - Lynn Houthuys; Johan A. K. Suykens (Inf. Fusion, 2021)

A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach. - Kevin Villalobos; Johan A. K. Suykens; Arantza Illarramendi (J. Intell. Manuf., 2021)

Generalization Properties of hyper-RKHS and its Applications. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (J. Mach. Learn. Res., 2021)

Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (Mach. Learn., 2021)

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. - Arun Pandey; Joachim Schreurs; Johan A. K. Suykens (Neural Networks, 2021)

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. - Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (Neural Networks, 2021)

Outlier detection in non-elliptical data by kernel MRCD. - Joachim Schreurs; Iwein Vranckx; Mia Hubert; Johan A. K. Suykens; Peter J. Rousseeuw (Stat. Comput., 2021)

Diversity Sampling is an Implicit Regularization for Kernel Methods. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2021)

Recurrent Restricted Kernel Machines for Time-series Forecasting. - Arun Pandey; Hannes De Meulemeester; Henri De Plaen; Bart De Moor; Johan A. K. Suykens (ESANN, 2022)

Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies. - Bram De Cooman; Johan A. K. Suykens; Andreas Ortseifen (LOD (2), 2022)

On the Double Descent of Random Features Models Trained with SGD. - Fanghui Liu 001; Johan A. K. Suykens; Volkan Cevher (NeurIPS, 2022)

Piecewise Linear Neural Networks and Deep Learning. - Qinghua Tao; Li Li 013; Xiaolin Huang; Xiangming Xi; Shuning Wang; Johan A. K. Suykens (CoRR, 2022)

Nyström landmark sampling and regularized Christoffel functions. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (Mach. Learn., 2022)

Disentangled Representation Learning and Generation With Manifold Optimization. - Arun Pandey; Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (Neural Comput., 2022)

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. - Michaël Fanuel; Antoine Aspeel; Jean-Charles Delvenne; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2022)

Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network. - Qinghua Tao; Zhen Li 032; Jun Xu 008; Shu Lin 002; Bart De Schutter; Johan A. K. Suykens (IEEE Trans. Intell. Transp. Syst., 2022)

Toward Deep Adaptive Hinging Hyperplanes. - Qinghua Tao; Jun Xu 008; Zhen Li 032; Na Xie; Shuning Wang; Xiaoli Li 011; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2022)

Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2022)

Unbalanced Optimal Transport: A Unified Framework for Object Detection. - Henri De Plaen; Pierre-François De Plaen; Johan A. K. Suykens; Marc Proesmans; Tinne Tuytelaars; Luc Van Gool (CVPR, 2023)

Tensorized LSSVMS For Multitask Regression. - Jiani Liu 002; Qinghua Tao; Ce Zhu; Yipeng Liu 001; Johan A. K. Suykens (ICASSP, 2023)

Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. - Francesco Tonin; Alex Lambert; Panagiotis Patrinos; Johan A. K. Suykens (ICML, 2023)

Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. - Yingyi Chen; Qinghua Tao; Francesco Tonin; Johan A. K. Suykens (NeurIPS, 2023)

Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. - Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (PKDD/ECML Workshops (4), 2023)

Semi-Supervised Classification with Graph Convolutional Kernel Machines. - Sonny Achten; Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (CoRR, 2023)

CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities. - Konstantinos Kontras; Christos Chatzichristos; Huy Phan; Johan A. K. Suykens; Maarten De Vos (CoRR, 2023)

Duality in Multi-View Restricted Kernel Machines. - Sonny Achten; Arun Pandey; Hannes De Meulemeester; Bart De Moor; Johan A. K. Suykens (CoRR, 2023)

Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method. - Qinghua Tao; Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (CoRR, 2023)

Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions. - Joran Michiels; Maarten De Vos; Johan A. K. Suykens (CoRR, 2023)

A Dual Formulation for Probabilistic Principal Component Analysis. - Henri De Plaen; Johan A. K. Suykens (CoRR, 2023)

Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs. - Jiani Liu 002; Qinghua Tao; Ce Zhu; Yipeng Liu 001; Xiaolin Huang; Johan A. K. Suykens (CoRR, 2023)

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. - Fan He; Mingzhen He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (CoRR, 2023)

Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems. - Mihaly Novak; Rocco Langone; Carlos Alzate; Johan A. K. Suykens (CoRR, 2023)

Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting. - Konstantinos Theodorakos; Oscar Mauricio Agudelo; Joachim Schreurs; Johan A. K. Suykens; Bart De Moor (IEEE Trans. Evol. Comput., 2023)

Jigsaw-ViT: Learning jigsaw puzzles in vision transformer. - Yingyi Chen; Xi Shen 001; Yahui Liu; Qinghua Tao; Johan A. K. Suykens (Pattern Recognit. Lett., 2023)

Learning With Asymmetric Kernels: Least Squares and Feature Interpretation. - Mingzhen He; Fan He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Multi-view kernel PCA for time series forecasting. - Arun Pandey; Hannes De Meulemeester; Bart De Moor; Johan A. K. Suykens (Neurocomputing, 2023)

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification. - Sonny Achten; Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (AAAI, 2024)

Sparsity via Sparse Group k-max Regularization. - Qinghua Tao; Xiangming Xi; Jun Xu 008; Johan A. K. Suykens (ACC, 2024)

Feature Learning using Multi-view Kernel Partial Least Squares. - Xinjie Zeng; Qinghua Tao; Johan A. K. Suykens (ESANN, 2024)

Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. - Yingyi Chen; Qinghua Tao; Francesco Tonin; Johan A. K. Suykens (ICML, 2024)

Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. - Qinghua Tao; Francesco Tonin; Alex Lambert; Yingyi Chen; Panagiotis Patrinos; Johan A. K. Suykens (ICML, 2024)

Nonlinear functional regression by functional deep neural network with kernel embedding. - Zhongjie Shi; Jun Fan; Linhao Song; Ding-Xuan Zhou; Johan A. K. Suykens (CoRR, 2024)

Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. - Zhongjie Shi; Fanghui Liu 001; Yuan Cao; Johan A. K. Suykens (CoRR, 2024)

A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. - Bram De Cooman; Johan A. K. Suykens (CoRR, 2024)

SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe. - Joris Depoortere; Johan Driesen; Johan A. K. Suykens; Hussain Syed Kazmi (CoRR, 2024)

HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity. - Sonny Achten; Francesco Tonin; Volkan Cevher; Johan A. K. Suykens (CoRR, 2024)

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. - Fan He; Mingzhen He; Lei Shi 010; Xiaolin Huang; Johan A. K. Suykens (CoRR, 2024)

Explaining the model and feature dependencies by decomposition of the Shapley value. - Joran Michiels; Johan A. K. Suykens; Maarten De Vos (Decis. Support Syst., 2024)

Tensor-based multi-view spectral clustering via shared latent space. - Qinghua Tao; Francesco Tonin; Panagiotis Patrinos; Johan A. K. Suykens (Inf. Fusion, 2024)

Deep Kernel Principal Component Analysis for multi-level feature learning. - Francesco Tonin; Qinghua Tao; Panagiotis Patrinos; Johan A. K. Suykens (Neural Networks, 2024)

Compressing Features for Learning With Noisy Labels. - Yingyi Chen; Shell Xu Hu; Xi Shen 001; Chunrong Ai; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2024)

Generative Kernel Spectral Clustering. - David Winant; Sonny Achten; Johan A. K. Suykens (CoRR, 2025)

Accelerating Spectral Clustering under Fairness Constraints. - Francesco Tonin; Alex Lambert; Johan A. K. Suykens; Volkan Cevher (CoRR, 2025)