José M. Peña

发表

Combinatonal Optimization by Learning and Simulation of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, Ramon Etxeberria, 2000, UAI.

Alternative Markov Properties for Acyclic Directed Mixed Graphs pdf

José M. Peña, J. Peña, 2015, ArXiv.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2005, Evolutionary Computation.

An empirical comparison of four initialization methods for the K-Means algorithm

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Editorial

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2005, Machine Learning.

Learning Acyclic Directed Mixed Graphs from Observations and Interventions

José M. Peña, 2016, Probabilistic Graphical Models.

On the Complexity of Discrete Feature Selection for Optimal Classification

José M. Peña, Roland Nilsson, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs pdf

José M. Peña, J. Peña, 2015, ECSQARU.

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2000, Pattern Recognit. Lett..

Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs

José M. Peña, 2016, UAI.

Approximate Counting of Graphical Models via MCMC Revisited pdf

José M. Peña, 2013, CAEPIA.

Marginal AMP chain graphs pdf

José M. Peña, J. Peña, 2013, Int. J. Approx. Reason..

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, AISTATS.

Every LWF and AMP Chain Graph Originates from a Set of Causal Models

José M. Peña, J. Peña, 2013, ECSQARU.

Gated Bayesian Networks

José M. Peña, Marcus Bendtsen, J. Peña, 2013, SCAI.

Growing Bayesian network models of gene networks from seed genes

Jesper Tegnér, José M. Peña, Johan Björkegren, 2005, ECCB/JBI.

Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption

Jesper Tegnér, José M. Peña, Johan Björkegren, 2005, ECSQARU.

DemocraticOP: A Democratic way of aggregating Bayesian network parameters

Kobra Etminani, Mahmoud Naghibzadeh, José M. Peña, 2013, Int. J. Approx. Reason..

Learning AMP chain graphs and some marginal models thereof under faithfulness pdf

José M. Peña, 2014, Int. J. Approx. Reason..

Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206]

José M. Peña, 2015, Int. J. Approx. Reason..

Performance evaluation of compromise conditional Gaussian networks for data clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, Int. J. Approx. Reason..

Gated Bayesian networks for algorithmic trading

José M. Peña, Marcus Bendtsen, 2016, Int. J. Approx. Reason..

On expressiveness of the chain graph interpretations

José M. Peña, Dag Sonntag, J. Peña, 2016, Int. J. Approx. Reason..

Error AMP Chain Graphs

José M. Peña, J. Peña, 2013, SCAI.

Towards Optimal Learning of Chain Graphs pdf

José M. Peña, J. Peña, 2011, ArXiv.

Reading dependencies from covariance graphs pdf

José M. Peña, J. Peña, 2010, Int. J. Approx. Reason..

Faithfulness in Chain Graphs: The Gaussian Case

José M. Peña, 2011, AISTATS.

Learning Marginal AMP Chain Graphs under Faithfulness

José M. Peña, J. Peña, 2014, Probabilistic Graphical Models.

Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control

José M. Peña, J. Peña, 2008, EvoBIO.

Towards scalable and data efficient learning of Markov boundaries

Jesper Tegnér, José M. Peña, Roland Nilsson, 2007, Int. J. Approx. Reason..

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2002, Machine Learning.

Chain Graph Interpretations and Their Relations

José M. Peña, Dag Sonntag, J. Peña, 2013, ECSQARU.

A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes" pdf

José M. Peña, 2011, ArXiv.

Learning dynamic Bayesian network models via cross-validation

Jesper Tegnér, José M. Peña, Johan Björkegren, 2005, Pattern Recognit. Lett..

Learning Bayesian networks for clustering by means of constructive induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Learning Optimal Chain Graphs with Answer Set Programming

Matti Järvisalo, José M. Peña, Antti Hyttinen, 2015, UAI.

On Local Optima in Learning Bayesian Networks pdf

José M. Peña, Tomas Kocka, Jens Dalgaard Nielsen, 2003, UAI.

Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity

Jesper Tegnér, José M. Peña, Roland Nilsson, 2009, Probabilistic Graphical Models.

Chain Graphs and Gene Networks

José M. Peña, Dag Sonntag, 2015, Foundations of Biomedical Knowledge Representation.

Evaluating Feature Selection for SVMs in High Dimensions

Jesper Tegnér, José M. Peña, Roland Nilsson, 2006, ECML.

Faithfulness in chain graphs: The discrete case

José M. Peña, J. Peña, 2009, Int. J. Approx. Reason..

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

María S. Pérez-Hernández, Pedro Larrañaga, José M. Peña, 2006, Towards a New Evolutionary Computation.

A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the 'Cipolla Rossa di Tropea' (Italy)

Giuseppe Modica, José M. Peña, Marco Vizzari, 2020, Remote. Sens..

Learning Gated Bayesian Networks for Algorithmic Trading

José M. Peña, Marcus Bendtsen, J. Peña, 2014, Probabilistic Graphical Models.

Optimizing the Design of Composite Panels using an Improved Genetic Algorithm

José M. Peña, Antonio LaTorre, V ´ õctor Robles, 2008 .

Identifying the Relevant Nodes Without Learning the Model pdf

Jesper Tegnér, José M. Peña, Roland Nilsson, 2006, UAI.

Learning AMP Chain Graphs under Faithfulness pdf

José M. Peña, 2012, PGM 2012.

An inclusion optimal algorithm for chain graph structure learning

José M. Peña, Jens Dalgaard Nielsen, Dag Sonntag, 2014, AISTATS.

Finding Consensus Bayesian Network Structures

José M. Peña, J. Peña, 2011, J. Artif. Intell. Res..

Approximate Counting of Graphical Models Via MCMC

José M. Peña, J. Peña, 2007, AISTATS.

Reading Dependencies from Polytree-Like Bayesian Networks

José M. Peña, J. Peña, 2007, UAI.

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2004, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Learning marginal AMP chain graphs under faithfulness revisited

José M. Peña, Manuel Gómez-Olmedo, J. Peña, 2016, Int. J. Approx. Reason..

Consistent Feature Selection for Pattern Recognition in Polynomial Time

Jesper Tegnér, José M. Peña, Roland Nilsson, 2007, J. Mach. Learn. Res..

Modelling regimes with Bayesian network mixtures

José M. Peña, Marcus Bendtsen, 2017, SAIS.

Approximate Counting of Graphical Models via MCMC Revisited

José M. Peña, Manuel Gómez-Olmedo, Dag Sonntag, 2015, Int. J. Intell. Syst..

Buildings Generated Using Variable Neighborhood Search

José M. Peña, Antonio LaTorre, Santiago Muelas, 2014 .

Causal effect identification in acyclic directed mixed graphs and gated models

José M. Peña, Marcus Bendtsen, 2017, Int. J. Approx. Reason..

DMGA: A Generic Brokering‐Based Data Mining Grid Architecture

Alberto Sánchez, Pilar Herrero, Pierre Gueant, 2009 .

Chain graph interpretations and their relations revisited

José M. Peña, Dag Sonntag, J. Peña, 2015, Int. J. Approx. Reason..

Detecting multivariate differentially expressed genes

Jesper Tegnér, José M. Peña, Roland Nilsson, 2007, BMC Bioinformatics.

3D segmentation of neuronal nuclei and cell-type identification using multi-channel information

Javier DeFelipe, José M. Peña, Antonio LaTorre, 2021, Expert Syst. Appl..

Modelling Evolution of Regulatory Networks in Artificial Bacteria

José M. Peña, Guillaume Beslon, David P. Parsons, 2008 .

A Multiagent Infrastructure for Data-Intensive Grid Applications

Víctor Robles, José M. Peña, Jesus Carretero, 2005 .

Interval Estimation Na¨ ive Bayes

Víctor Robles, José M. Peña, Ernestina Menasalvas, 2003 .

Minimal decision rules based on the apriori algorithm

José M. Peña, Ernestina Menasalvas, Socorro Millán, 2001 .

A Model of the Spatially Dependent Mechanical Properties of the Axon During Its Growth

José M. Peña, Antoine Jérusalem, J. Peña, 2012 .

A computational model coupling mechanics and electrophysiology in spinal cord injury

José M. Peña, Julián A. García-Grajales, Antoine Jérusalem, 2014, Biomechanics and modeling in mechanobiology.