Profesor titular
Ph.D. in Computer Science
Intelligent Systems Group
Department of Computer Science and Artificial Intelligence
University of the Basque Country
Donostia-San Sebastian
Spain

post: Jose Antonio Lozano   P.O. Box 649 20080 Donostia - San Sebastián Basque Country, Spain	email: lozano at si.ehu.es
phone:     +34 943 018029	fax:     +34 943 015590

• Machine Learning and Search Heuristics in general
  
• Probabilistic Graphical Models in general (PGMs)
  
• Applications in Medicine and Bioinformatics: DNA microarrays in classification tasks
  
• Search Heuristics: Estimation of Distribution Algorithms (EDAs)

For a complete list of publications consult the journals' publication list of the group.

• Edited books:
  • J.A. Lozano, P. Larrañaga, I. Inza, E. Bengoetxea (2005). Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms. Springer Verlag. In press.
    
  • P. Larrañaga, J.A. Lozano (2002). Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers.
  
  
• Journals:
  • P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (guest editors, 2005). Special Issue on Probabilistic Graphical Models for Classification. Machine Learning.
    
  • J.A. Lozano, P. Larrañaga (guest editors, 2005). Special Issue on Estimation of Distribution Algorithms. Evolutionary Computation.
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2005). Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks. Evolutionary Computation. Accepted for publication.
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2004). Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 63-82.
  • C. González, J.A. Lozano, P. Larrañaga (2002). Mathematical modeling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. International Journal of Approximate Reasoning, 31, 313-340.
  • P. Larrañaga, J.A. Lozano (2002). Synergies between evolutionary computation and probabilistic graphical models. International Journal of Approximate Reasoning, 31, 155-156.
  • J. M. Peña, J. A. Lozano, P. Larrañaga (2002). Learning recursive Bayesian multinets for clustering by means of constructive induction. Machine Learning, 47(1), 63-90.
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2001). Performance evaluation of compromise conditional Gaussian networks for data clustering. International Journal of Approximate Reasoning, 28(1), 23-50.
    
  • C. González, J.A. Lozano, P. Larrañaga (2001). Analyzing the PBIL algorithm by means of discrete dynamical systems. Complex Systems, 12(4), 465-479.
    
  • J. M. Peña, J. A. Lozano, P. Larrañaga (2001). Performance Evaluation of Compromise Conditional Gaussian Networks for Data Clustering. International Journal of Approximate Reasoning, 28(1), 23-50.
    
  • J. M. Peña, J. A. Lozano, P. Larrañaga, I. Inza (2001). Dimensionality reduction in unsupervised learning of conditional Gaussian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 590-603.
    
  • C. González, J.A. Lozano, P. Larrañaga (2000). Analyzing the PBIL algorithm by means of discrete dynamical systems. Complex Systems 12(4), 465-479.
    
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2000). An improved Bayesian Structural EM algorithm for learning Bayesian Networks for clustering. Pattern Recognition Letters 21(8), 779-786.
    
  • J. A. Lozano, P. Larrañaga (1999). Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recognition Letters, 20, 911-918.
    
  • J.M. Peña, J.A. Lozano, P. Larrañaga (1999). Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognition Letters 20 (11-13), 1219-1230.
    
  • I. Inza, P. Larrañaga, B. Sierra, R. Etxeberria, J.A. Lozano, J.M. Peña (1999). Representing the joint behaviour of Machine Learning inducers by Bayesian Networks. Pattern Recognition Letters 20 (11-13), 1201-1209.
    
  • J. M. Peña, J. A. Lozano, P. Larrañaga (1999). An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters 20 (10), 1027-1040.
    
  • J. A. Lozano, P. Larrañaga (1999). Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recognition Letters 20 (9), 911-918.
    
  • J. A. Lozano, P. Larrañaga, M. Graña, F. X. Albizuri (1999). Genetic algorithms: bridging the convergence gap. Theoretical Computer Science 229, 11-22.
    
  • I. Inza, P. Larrañaga, B. Sierra, R. Etxeberria, J.A. Lozano, J.M. Peña (1999). Representing the joint behaviour of machine learning inducers by Bayesian networks. Pattern Recognition Letters, 20(11-13), 1201-1209.
  • J.M. Peña, J.A. Lozano, P. Larrañaga (1999). Constructive induction for clustering: naive-Bayes models to mixtures of Bayesian networks.Pattern Recognition Letters, 20(11-13), 1219-1230
    
  • J.M. Peña, J. A. Lozano, P. Larrañaga (1999). An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20(10), 1027-1040.
    
  • F.X. Albizuri, A. D'Anjou, M. Graña, J.A. Lozano (1996). Convergence Properties of High-order Boltzmann Machines.Neural Networks, 9(9), 1561-1567
  
  
• Conference papers:
  • I. Inza, P. Larranaga, B. Sierra, R. Etxeberria, J.A. Lozano, J.M. Peña (1999). Representing the joint behaviour of Machine Learning inducers by Bayesian Networks. Pattern Recognition in Practice VI. Vieland, The Netherlands.
    
  • P. Larranaga, R. Etxeberria, J.A. Lozano, B. Sierra, I. Inza, J.M. Peña (1999).A review of the cooperation between evolutionary computation and probabilistic graphical models. Proceedings of the Second Symposium on Artificial Intelligence. Adaptive Systems. CIMAF 99. Special Session on Distributions and Evolutionary Computation, 314-324. La Habana.
  • J.M. Peña, J.A. Lozano and P. Larrañaga (1999). Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognition in Practice VI. Vieland, The Netherlands.
  
  
• Technical reports:
  • P. Larrañaga, R. Etxeberria, J.A. Lozano, J.M. Peña (1999). Optimization by learning and simulation of Bayesian and Gaussian networks.Technical Report. EHU-KZAA-IK-4/99. University of the Basque Country.
    
  • C. González, J.A. Lozano, P. Larrañaga (1999). The convergence behavior of PBIL algorithm: a preliminar approach.Technical Report. EHU-KZAA-IK-3/99. University of the Basque Country.
    
  
  
• Book chapters:
  • J.A. Lozano, P. Larrañaga, M. Graña (1998).Partitional cluster analysis with genetic algorithms: searching for the number of clusters. Studies in Classification, Data Analysis and Knowledge Organization: Data Science Classification and Related Methods. C. Hayaski, N. Ohsumi, K. Yajima, Y. Tanaka, H. H. Boek, Y. Baba (eds.), 117-124. Springer-Verlag.