Analytical Learning Guided by Empirical Technology: An Approach to Integration (Extended Abstract). - Nikolay I. Nikolaev; Evgueni N. Smirnov (ECML, 1995)

Multiple Explanation-based Learning Guided by Space Fragmenting. - Evgueni N. Smirnov; Nikolay I. Nikolaev (SCAI, 1995)

Stochastically Guided Disjunctive Version Space Learning. - Nikolay I. Nikolaev; Evgueni N. Smirnov (ECAI, 1996)

Fitness Landscapes and Inductive Genetic Programming. - Vanio Slavov; Nikolay I. Nikolaev (ICANNGA, 1997)

Inductive Genetic Programming and Superposition of Fitness Landscapes. - Vanio Slavov; Nikolay I. Nikolaev (ICGA, 1997)

Concepts of Inductive Genetic Programming. - Nikolay I. Nikolaev; Vanio Slavov (EuroGP, 1998)

Genetic Algorithms, Fitness Sublandscapes and Subpopulations. - Vanio Slavov; Nikolay I. Nikolaev (FOGA, 1998)

Immune Network Dynamics for Inductive Problem Solving. - Vanio Slavov; Nikolay I. Nikolaev (PPSN, 1998)

Inductive Genetic Programming with Decision Trees. - Nikolay I. Nikolaev; Vanio Slavov (Intell. Data Anal., 1998)

Automated Discovery of Polynomials by Inductive Genetic Programming. - Nikolay I. Nikolaev; Hitoshi Iba (PKDD, 1999)

Genetic programming of polynomial harmonic models using the discrete Fourier transform. - Nikolay I. Nikolaev; Hitoshi Iba (CEC, 2001)

Accelerated Genetic Programming of Polynomials. - Nikolay I. Nikolaev; Hitoshi Iba (Genet. Program. Evolvable Mach., 2001)

Genetic Programming and Data Structures: Genetic Programming+Data Structures=Automatic Programming. - Nikolay I. Nikolaev (Softw. Focus, 2001)

Regularization approach to inductive genetic programming. - Nikolay I. Nikolaev; Hitoshi Iba (IEEE Trans. Evol. Comput., 2001)

Overfitting avoidance in genetic programming of polynomials. - Nikolay I. Nikolaev; Lilian M. de Menezes; Hitoshi Iba (IEEE Congress on Evolutionary Computation, 2002)

Genetic Programming of Polynomial Harmonic Networks Using the Discrete Fourier Transform. - Nikolay I. Nikolaev; Hitoshi Iba (Int. J. Neural Syst., 2002)

Polynomial harmonic GMDH learning networks for time series modeling. - Nikolay I. Nikolaev; Hitoshi Iba (Neural Networks, 2003)

Learning polynomial feedforward neural networks by genetic programming and backpropagation. - Nikolay Y. Nikolaev; Hitoshi Iba (IEEE Trans. Neural Networks, 2003)

Sequential relevance vector machine learning from time series. - Nikolay I. Nikolaev; Peter Tino (IJCNN, 2005)

Generalizing Version Space Support Vector Machines for Non-Separable Data. - Evgueni N. Smirnov; Ida G. Sprinkhuizen-Kuyper; Nikolay I. Nikolaev (ICDM Workshops, 2006)

A One-Step Unscented Particle Filter for Nonlinear Dynamical Systems. - Nikolay Y. Nikolaev; Evgueni N. Smirnov (ICANN (1), 2007)

Recursive Bayesian Levenberg-Marquardt Training of Recurrent Neural Networks. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (IJCNN, 2007)

Recurrent Expectation Maximization Neural Modeling. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (CIMCA/IAWTIC/ISE, 2008)

Dynamic Modeling with Ensemble Kalman Filter Trained Recurrent Neural Networks. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (ICMLA, 2008)

Description Identification and the Consistency Problem. - Evgueni N. Smirnov; Nikolay Y. Nikolaev; Georgi I. Nalbantov (SGAI Conf., 2008)

Sequential Bayesian kernel modelling with non-Gaussian noise. - Nikolay Y. Nikolaev; Lilian M. de Menezes (Neural Networks, 2008)

Single-Stacking Conformity Approach to Reliable Classification. - Evgueni N. Smirnov; Nikolay I. Nikolaev; Georgi I. Nalbantov (AIMSA, 2010)

Unscented grid filtering and elman recurrent networks. - Nikolay Y. Nikolaev; Derrick Takeshi Mirikitani; Evgueni N. Smirnov (IJCNN, 2010)

k-Version-Space Multi-class Classification Based on k-Consistency Tests. - Evgueni N. Smirnov; Georgi I. Nalbantov; Nikolay I. Nikolaev (ECML/PKDD (3), 2010)

Efficient online recurrent connectionist learning with the ensemble Kalman filter. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (Neurocomputing, 2010)

Recursive Bayesian recurrent neural networks for time-series modeling. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (IEEE Trans. Neural Networks, 2010)

Time-Dependent Series Variance Estimation via Recurrent Neural Networks. - Nikolay I. Nikolaev; Peter Tiño; Evgueni N. Smirnov (ICANN (1), 2011)

Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks. - Derrick Takeshi Mirikitani; Nikolay I. Nikolaev (Neural Comput. Appl., 2011)

Analytical factor stochastic volatility modeling for portfolio allocation. - Nikolay Y. Nikolaev; Evgueni N. Smirnov (CIFEr, 2012)

Aggregating Human-Expert Opinions for Multi-Label Classification. - Evgueni N. Smirnov; Hua Zhang; Ralf Peeters; Nikolay I. Nikolaev; Maike Imkamp (HCOMP (Works in Progress / Demos), 2013)

Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation. - Nikolay Y. Nikolaev; Georgi N. Boshnakov; Robert Zimmer (Expert Syst. Appl., 2013)

Time-dependent series variance learning with recurrent mixture density networks. - Nikolay I. Nikolaev; Peter Tiño; Evgueni N. Smirnov (Neurocomputing, 2013)

Nonlinear filtering of asymmetric stochastic volatility models and Value-at-Risk estimation. - Nikolay Y. Nikolaev; Lilian M. de Menezes; Evgueni N. Smirnov (CIFEr, 2014)

Instance-Based Decompositions of Error Correcting Output Codes. - Firat Ismailoglu; Evgueni N. Smirnov; Nikolay Y. Nikolaev; Ralf Peeters (MCS, 2015)

A regime-switching recurrent neural network model applied to wind time series. - Nikolay Y. Nikolaev; Evgueni N. Smirnov; Daniel Stamate; Robert Zimmer (Appl. Soft Comput., 2019)

Multi-view Semi-supervised Learning Using Privileged Information. - Evgueni N. Smirnov; Richard Delava; Ron Diris; Nikolay I. Nikolaev (EANN, 2023)