| An empirical comparison of four initialization methods for the k-means algorithm JM Pena, JA Lozano, P Larranaga Pattern recognition letters 20 (10), 1027-1040, 1999 | 1127 | 1999 |
| Optimization in continuous domains by learning and simulation of Gaussian networks P Larrañaga, R Etxeberria, JA Lozano, JM Peña | 280 | 2000 |
| Towards scalable and data efficient learning of Markov boundaries JM Pena, R Nilsson, J Björkegren, J Tegnér International Journal of Approximate Reasoning 45 (2), 211-232, 2007 | 240 | 2007 |
| Optimization by learning and simulation of Bayesian and Gaussian networks P Larranaga 1999 EHU-KZAAIK-4/99, University of the Basque Country, 1999 | 233 | 1999 |
| Consistent feature selection for pattern recognition in polynomial time R Nilsson, JM Pena, J Björkegren, J Tegnér The Journal of Machine Learning Research 8, 589-612, 2007 | 202 | 2007 |
| Combinatorial optimization by learning and simulation of Bayesian networks P Larranaga, R Etxeberria, JA Lozano, JM Pena arXiv preprint arXiv:1301.3871, 2013 | 171 | 2013 |
| Growing Bayesian network models of gene networks from seed genes JM Pena, J Björkegren, J Tegnér Bioinformatics 21 (suppl_2), ii224-ii229, 2005 | 91 | 2005 |
| Dimensionality reduction in unsupervised learning of conditional Gaussian networks JM Pena, JA Lozano, P Larranaga, I Inza IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (6), 590-603, 2001 | 80 | 2001 |
| An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering JM Peña, JA Lozano, P Larrañaga Pattern Recognition Letters 21 (8), 779-786, 2000 | 76 | 2000 |
| Learning recursive Bayesian multinets for data clustering by means of constructive induction JM Peña, JA Lozano, P Larrañaga Machine Learning 47, 63-89, 2002 | 67 | 2002 |
| On local optima in learning Bayesian networks JD Nielsen, T Kocka, JM Pena arXiv preprint arXiv:1212.2500, 2012 | 65 | 2012 |
| Unsupervised feature subset selection N Søndberg-Madsen, C Thomsen, JM Pena Aalborg University. Department of Computer Science, 2003 | 61 | 2003 |
| Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks JM Peña, JA Lozano, P Larrañaga Evolutionary Computation 13 (1), 43-66, 2005 | 52 | 2005 |
| Learning gaussian graphical models of gene networks with false discovery rate control JM Pena Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics …, 2008 | 44 | 2008 |
| Learning dynamic Bayesian network models via cross-validation JM Pena, J Björkegren, J Tegnér Pattern Recognition Letters 26 (14), 2295-2308, 2005 | 44 | 2005 |
| Evaluating feature selection for SVMs in high dimensions R Nilsson, JM Pena, J Björkegren, J Tegnér Machine Learning: ECML 2006: 17th European Conference on Machine Learning …, 2006 | 40 | 2006 |
| Scalable, efficient and correct learning of Markov boundaries under the faithfulness assumption JM Pena, J Björkegren, J Tegnér Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 8th …, 2005 | 40 | 2005 |
| Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering JM Pena, JA Lozano, P Larranaga International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2004 | 36 | 2004 |
| Detecting multivariate differentially expressed genes R Nilsson, JM Peña, J Björkegren, J Tegnér BMC bioinformatics 8 (1), 1-10, 2007 | 35 | 2007 |
| Finding consensus Bayesian network structures JM Pena Journal of Artificial Intelligence Research 42, 661-687, 2011 | 30 | 2011 |
Pedro LarrañagaProfessor of Artificial Intelligence - Technical University of MadridBestätigte E-Mail-Adresse bei fi.upm.es
