Peter Tiño

According to our database1, Peter Tiño authored at least 153 papers between 1995 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2019
A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks.
Evolutionary Computation, 2019

Dynamical Systems as Temporal Feature Spaces.
CoRR, 2019

Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets.
CoRR, 2019

A mixture of experts model for predicting persistent weather patterns.
CoRR, 2019

Feature Relevance Bounds for Ordinal Regression.
CoRR, 2019

Coevolutionary systems and PageRank.
Artif. Intell., 2019

Extended stochastic derivative-free optimization on riemannian manifolds.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018
Asymptotic Fisher memory of randomized linear symmetric Echo State Networks.
Neurocomputing, 2018

Supervised low rank indefinite kernel approximation using minimum enclosing balls.
Neurocomputing, 2018

Sparsification of Indefinite Learning Models.
Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2018

Randomized Recurrent Neural Networks.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Machine learning and data analysis in astroinformatics.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017
Indefinite Core Vector Machine.
Pattern Recognition, 2017

Ordinal regression based on learning vector quantization.
Neural Networks, 2017

Linking Twitter Events With Stock Market Jitters.
CoRR, 2017

Probabilistic matching: Causal inference under measurement errors.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Classification of sparsely and irregularly sampled time series: A learning in model space approach.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Linear dynamical based models for sequential domains.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services.
Proceedings of the 2017 IEEE International Conference on Web Services, 2017

Fisher memory of linear Wigner echo state networks.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016
Oversampling the Minority Class in the Feature Space.
IEEE Trans. Neural Netw. Learning Syst., 2016

Model-coupled autoencoder for time series visualisation.
Neurocomputing, 2016

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.
Front. Comput. Neurosci., 2016

Probabilistic classifiers with low rank indefinite kernels.
CoRR, 2016

Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data.
Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

Interaction-Awareness for Self-Adaptive Volunteer Computing.
Proceedings of the 10th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2016

Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2016, 2016

Learning in indefinite proximity spaces - recent trends.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation.
Proceedings of the Artificial Neural Networks in Pattern Recognition, 2016

2015
Artificial Neural Network Models.
Proceedings of the Springer Handbook of Computational Intelligence, 2015

The Benefits of Modeling Slack Variables in SVMs.
Neural Computation, 2015

Indefinite Proximity Learning: A Review.
Neural Computation, 2015

Autoencoding Time Series for Visualisation.
CoRR, 2015

Large Scale Indefinite Kernel Fisher Discriminant.
Proceedings of the Similarity-Based Pattern Recognition - Third International Workshop, 2015

Incremental probabilistic classification vector machine with linear costs.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Model Metric Co-Learning for Time Series Classification.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness.
Proceedings of the 2015 IEEE International Conference on Web Services, 2015

Automated Detection of Galaxy Groups Through Probabilistic Hough Transform.
Proceedings of the Neural Information Processing - 22nd International Conference, 2015

Probabilistic Classification Vector Machine at large scale.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Autoencoding time series for visualisation.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014
Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection.
IEEE Trans. Neural Netw. Learning Syst., 2014

Learning in the Model Space for Cognitive Fault Diagnosis.
IEEE Trans. Neural Netw. Learning Syst., 2014

Dimensionality reduction and topographic mapping of binary tensors.
Pattern Anal. Appl., 2014

Ordinal regression neural networks based on concentric hyperspheres.
Neural Networks, 2014

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.
NeuroImage, 2014

Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network.
Eng. Appl. of AI, 2014

Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space.
Computers & Chemical Engineering, 2014

A Utility Model for Volunteered Service Composition.
Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing, 2014

Learning the deterministically constructed Echo State Networks.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Towards Self-Aware Service Composition.
Proceedings of the 2014 IEEE International Conference on High Performance Computing and Communications, 2014

Support Vector Ordinal Regression using Privileged Information.
Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Recent trends in learning of structured and non-standard data.
Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013
A Framework for the Analysis of Process Mining Algorithms.
IEEE Trans. Systems, Man, and Cybernetics: Systems, 2013

Incorporating Privileged Information Through Metric Learning.
IEEE Trans. Neural Netw. Learning Syst., 2013

Complex Coevolutionary Dynamics - Structural Stability and Finite Population Effects.
IEEE Trans. Evolutionary Computation, 2013

Scaling Up Estimation of Distribution Algorithms for Continuous Optimization.
IEEE Trans. Evolutionary Computation, 2013

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models.
IEEE Trans. Biomed. Engineering, 2013

Exploitation of Pairwise Class Distances for Ordinal Classification.
Neural Computation, 2013

Short term memory in input-driven linear dynamical systems.
Neurocomputing, 2013

Time-dependent series variance learning with recurrent mixture density networks.
Neurocomputing, 2013

Novel approaches in machine learning and computational intelligence.
Neurocomputing, 2013

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get?
Entropy, 2013

Degree distribution and scaling in the Connecting Nearest Neighbors model
CoRR, 2013

A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Model-based kernel for efficient time series analysis.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Concept drift detection for online class imbalance learning.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Ordinal-based metric learning for learning using privileged information.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

A principled approach to mining from noisy logs using Heuristics Miner.
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2013

2012
Improving Generalization Performance in Co-Evolutionary Learning.
IEEE Trans. Evolutionary Computation, 2012

Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps.
Neural Computation, 2012

Adaptive Metric Learning Vector Quantization for Ordinal Classification.
Neural Computation, 2012

Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks.
MONET, 2012

Learning in the Model Space for Fault Diagnosis
CoRR, 2012

Computational Intelligence in Astronomy - A Win-Win Situation.
Proceedings of the Theory and Practice of Natural Computing, 2012

Prototype Based Modelling for Ordinal Classification.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2012, 2012

Learning Using Privileged Information in Prototype Based Models.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Process Mining in Non-Stationary Environments.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Theory of Input Driven Dynamical Systems.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011
Minimum Complexity Echo State Network.
IEEE Trans. Neural Networks, 2011

Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform.
IEEE/ACM Trans. Comput. Biology Bioinform., 2011

Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis.
Proceedings of the Mathematical and Engineering Methods in Computer Science, 2011

One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression.
Proceedings of the Advances in Neural Networks - ISNN 2011, 2011

A Principled Approach to the Analysis of Process Mining Algorithms.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2011, 2011

Real-Time Detection of Process Change using Process Mining.
Proceedings of the 2011 Imperial College Computing Student Workshop, 2011

Time-Dependent Series Variance Estimation via Recurrent Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Negatively Correlated Echo State Networks.
Proceedings of the ESANN 2011, 2011

2010
Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application.
Pattern Recognition, 2010

Adapting to NAT timeout values in P2P overlay networks.
Proceedings of the 24th IEEE International Symposium on Parallel and Distributed Processing, 2010

Simple Deterministically Constructed Recurrent Neural Networks.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2010

Multilinear Decomposition and Topographic Mapping of Binary Tensors.
Proceedings of the Artificial Neural Networks - ICANN 2010, 2010

On Reliability Of Simulations Of Complex Co-Evolutionary Processes.
Proceedings of the European Conference on Modelling and Simulation, 2010

One-shot Learning of Poisson Distributions in fast changing environments.
Proceedings of the Learning paradigms in dynamic environments, 25.07. - 30.07.2010, 2010

2009
Probabilistic Classification Vector Machines.
IEEE Trans. Neural Networks, 2009

Predictive Ensemble Pruning by Expectation Propagation.
IEEE Trans. Knowl. Data Eng., 2009

Relationship Between Generalization and Diversity in Coevolutionary Learning.
IEEE Trans. Comput. Intellig. and AI in Games, 2009

Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays.
BMC Bioinformatics, 2009

Fast parzen window density estimator.
Proceedings of the International Joint Conference on Neural Networks, 2009

Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data.
Proceedings of the International Joint Conferences on Bioinformatics, 2009

Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model.
Proceedings of the Artificial Neural Networks, 2009

Estimating Time Delay in Gravitationally Lensed Fluxes.
Proceedings of the Similarity-based learning on structures, 15.02. - 20.02.2009, 2009

Visualization of Structured Data via Generative Probabilistic Modeling.
Proceedings of the Similarity-Based Clustering, 2009

Still alive: Extending keep-alive intervals in P2P overlay networks.
Proceedings of the 5th International Conference on Collaborative Computing: Networking, 2009

2008
Visualization of Tree-Structured Data Through Generative Topographic Mapping.
IEEE Trans. Neural Networks, 2008

Measuring Generalization Performance in Coevolutionary Learning.
IEEE Trans. Evolutionary Computation, 2008

Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Predictive Modeling with Echo State Networks.
Proceedings of the Artificial Neural Networks, 2008

2007
Markovian Bias of Neural-based Architectures With Feedback Connections.
Proceedings of the Perspectives of Neural-Symbolic Integration, 2007

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks.
Neural Computation, 2007

On Conditions for Intermittent Search in Self-organizing Neural Networks.
Proceedings of the MICAI 2007: Advances in Artificial Intelligence, 2007

Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling.
Proceedings of the IJCAI 2007, 2007

Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks.
Proceedings of the Neural Information Processing, 14th International Conference, 2007

Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences.
Proceedings of the Artificial Neural Networks, 2007

Visualisation of tree-structured data through generative probabilistic modelling.
Proceedings of the ESANN 2007, 2007

2006
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons.
Neural Computation, 2006

Dynamics and Topographic Organization of Recursive Self-Organizing Maps.
Neural Computation, 2006

How accurate are the time delay estimates in gravitational lensing?
CoRR, 2006

Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks.
Proceedings of the Parallel Problem Solving from Nature, 2006

Does Money Matter? An Artificial Intelligence Approach.
Proceedings of the 2006 Joint Conference on Information Sciences, 2006

A Probabilistic Ensemble Pruning Algorithm.
Proceedings of the Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses.
Proceedings of the Machine Learning: ECML 2006, 2006

2005
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization.
IEEE Trans. Knowl. Data Eng., 2005

Managing Diversity in Regression Ensembles.
J. Mach. Learn. Res., 2005

Recursive Self-organizing Map as a Contractive Iterative Function System.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2005

On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map.
Proceedings of the Advances in Natural Computation, First International Conference, 2005

2004
Markovian architectural bias of recurrent neural networks.
IEEE Trans. Neural Networks, 2004

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.
Neural Networks, 2004

Nonlinear Prediction of Quantitative Structure-Activity Relationships.
Journal of Chemical Information and Modeling, 2004

Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies.
Proceedings of the Parallel Problem Solving from Nature, 2004

A generative probabilistic approach to visualizing sets of symbolic sequences.
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Introducing a Star Topology into Latent Class Models for Collaborative Filtering.
Proceedings of the Artificial Intelligence Applications and Innovations, 2004

2003
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation, 2003

Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation, 2003

2002
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way.
IEEE Trans. Pattern Anal. Mach. Intell., 2002

A General Framework for a Principled Hierarchical Visualization of Multivariate Data.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2002

2001
Financial volatility trading using recurrent neural networks.
IEEE Trans. Neural Networks, 2001

Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series.
Pattern Anal. Appl., 2001

Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks).
Neural Computation, 2001

Predicting the Future of Discrete Sequences from Fractal Representations of the Past.
Machine Learning, 2001

Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds.
Proceedings of the Artificial Neural Networks, 2001

2000
Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

The profitability of trading volatility using real-valued and symbolic models.
Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering, 2000

1999
Spatial representation of symbolic sequences through iterative function systems.
IEEE Trans. Systems, Man, and Cybernetics, Part A, 1999

Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences.
IEEE Trans. Neural Networks, 1999

Building Predictive Models from Fractal Representations of Symbolic Sequences.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Graded Grammaticality in Prediction Fractal Machines.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998
Recurrent Neural Networks with Iterated Function Systems Dynamics.
Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), 1998

Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.
Proceedings of the Hybrid Neural Systems, 1998

1997
Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences.
Proceedings of the Knowledge-Based Intelligent Electronic Systems, 1997

1996
Learning long-term dependencies in NARX recurrent neural networks.
IEEE Trans. Neural Networks, 1996

1995
Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model.
Neural Computation, 1995

Learning long-term dependencies is not as difficult with NARX networks.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995


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