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• 
  João Gama, Rui Camacho, Pavel Brazdil, Alípio Jorge, Luís Torgo:
  Machine Learning: ECML 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings. Lecture Notes in Computer Science 3720, Springer 2005, ISBN 3-540-29243-8

(shared with PKDD 2005)

• 
  Michael R. Berthold:
  Data Analysis in the Life Sciences - Sparking Ideas -. 1
• 
  Claire Cardie:
  Machine Learning for Natural Language Processing (and Vice Versa?). 2
• 
  Luc De Raedt:
  Statistical Relational Learning: An Inductive Logic Programming Perspective. 3-5
• 
  Eamonn J. Keogh:
  Recent Advances in Mining Time Series Data. 6
• 
  Ron Kohavi:
  Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce. 7
• 
  Yossi Matias:
  Data Streams and Data Synopses for Massive Data Sets. 8-9

• 
  Liviu Badea:
  Clustering and Metaclustering with Nonnegative Matrix Decompositions. 10-22
• 
  Christian Bessiere, Remi Coletta, Frédéric Koriche, Barry O'Sullivan:
  A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems. 23-34
• 
  Steffen Bickel, Tobias Scheffer:
  Estimation of Mixture Models Using Co-EM. 35-46
• 
  Matthew Brand:
  Nonrigid Embeddings for Dimensionality Reduction. 47-59
• 
  Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
  Multi-view Discriminative Sequential Learning. 60-71
• 
  Jesús Cerquides, Ramón López de Mántaras:
  Robust Bayesian Linear Classifier Ensembles. 72-83
• 
  Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa:
  An Integrated Approach to Learning Bayesian Networks of Rules. 84-95
• 
  Isabel Drost, Tobias Scheffer:
  Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam. 96-107
• 
  Arkady Epshteyn, Gerald DeJong:
  Rotational Prior Knowledge for SVMs. 108-119
• 
  Stefano Ferilli, Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito:
  On the LearnAbility of Abstraction Theories from Observations for Relational Learning. 120-132
• 
  George Forman, Ira Cohen:
  Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers. 133-145
• 
  Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
  Kernel Basis Pursuit. 146-157
• 
  Iris Hendrickx, Antal van den Bosch:
  Hybrid Algorithms with Instance-Based Classification. 158-169
• 
  Aloak Kapoor, Russell Greiner:
  Learning and Classifying Under Hard Budgets. 170-181
• 
  Wolf Kienzle, Bernhard Schölkopf:
  Training Support Vector Machines with Multiple Equality Constraints. 182-193
• 
  Hans van Kuilenburg, Marco A. Wiering, Marten den Uyl:
  A Model Based Method for Automatic Facial Expression Recognition. 194-205
• 
  François Laviolette, Mario Marchand, Mohak Shah:
  Margin-Sparsity Trade-Off for the Set Covering Machine. 206-217
• 
  Xiaoli Li, Bing Liu:
  Learning from Positive and Unlabeled Examples with Different Data Distributions. 218-229
• 
  Shiau Hong Lim, Gerald DeJong:
  Towards Finite-Sample Convergence of Direct Reinforcement Learning. 230-241
• 
  Hsuan-Tien Lin, Ling Li:
  Infinite Ensemble Learning with Support Vector Machines. 242-254
• 
  Siwei Lyu:
  A Kernel Between Unordered Sets of Data: The Gaussian Mixture Approach. 255-267
• 
  Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney:
  Active Learning for Probability Estimation Using Jensen-Shannon Divergence. 268-279
• 
  Jan Peters, Sethu Vijayakumar, Stefan Schaal:
  Natural Actor-Critic. 280-291
• 
  Detlef Prescher:
  Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing. 292-304
• 
  Stefan Raeymaekers, Maurice Bruynooghe, Jan Van den Bussche:
  Learning (k, l)-Contextual Tree Languages for Information Extraction. 305-316
• 
  Martin A. Riedmiller:
  Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method. 317-328
• 
  Carsten Riggelsen:
  MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition. 329-340
• 
  Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski:
  On Discriminative Joint Density Modeling. 341-352
• 
  Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony:
  Model-Based Online Learning of POMDPs. 353-364
• 
  Shengli Sheng, Charles X. Ling, Qiang Yang:
  Simple Test Strategies for Cost-Sensitive Decision Trees. 365-376
• 
  JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin Ghahramani:
  U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models. 377-388
• 
  Daniel Szer, François Charpillet:
  An Optimal Best-First Search Algorithm for Solving Infinite Horizon DEC-POMDPs. 389-399
• 
  Kari Torkkola, Eugene Tuv:
  Ensemble Learning with Supervised Kernels. 400-411
• 
  Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin:
  Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. 412-424
• 
  Ronan Trepos, Ansaf Salleb, Marie-Odile Cordier, Véronique Masson, Chantal Gascuel:
  A Distance-Based Approach for Action Recommendation. 425-436
• 
  Joannès Vermorel, Mehryar Mohri:
  Multi-armed Bandit Algorithms and Empirical Evaluation. 437-448
• 
  Gang Wang, Zhihua Zhang, Frederick H. Lochovsky:
  Annealed Discriminant Analysis. 449-460
• 
  Shijun Wang, Changshui Zhang:
  Network Game and Boosting. 461-472
• 
  Olcay Taner Yildiz, Ethem Alpaydin:
  Model Selection in Omnivariate Decision Trees. 473-484
• 
  Marie desJardins, Priyang Rathod, Lise Getoor:
  Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence. 485-496

• 
  Steffen Bickel, Peter Haider, Tobias Scheffer:
  Learning to Complete Sentences. 497-504
• 
  Antoine Bordes, Léon Bottou:
  The Huller: A Simple and Efficient Online SVM. 505-512
• 
  Jérôme Callut, Pierre Dupont:
  Inducing Hidden Markov Models to Model Long-Term Dependencies. 513-521
• 
  François Coste, Goulven Kerbellec:
  A Similar Fragments Merging Approach to Learn Automata on Proteins. 522-529
• 
  Chris H. Q. Ding, Xiaofeng He, Horst D. Simon:
  Nonnegative Lagrangian Relaxation of K-Means and Spectral Clustering. 530-538
• 
  Chris Drummond, Robert C. Holte:
  Severe Class Imbalance: Why Better Algorithms Aren't the Answer. 539-546
• 
  Tapio Elomaa, Jussi Kujala, Juho Rousu:
  Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes. 547-555
• 
  Daan Fierens, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe:
  A Comparison of Approaches for Learning Probability Trees. 556-563
• 
  George Forman:
  Counting Positives Accurately Despite Inaccurate Classification. 564-575
• 
  Ramunas Girdziusas, Jorma Laaksonen:
  Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities. 576-583
• 
  Ali Hamzeh, Adel Rahmani:
  An Evolutionary Function Approximation Approach to Compute Prediction in XCSF. 584-592
• 
  Masoumeh T. Izadi, Doina Precup:
  Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes. 593-600
• 
  Robin Jaulmes, Joelle Pineau, Doina Precup:
  Active Learning in Partially Observable Markov Decision Processes. 601-608
• 
  Sergio Jiménez Celorrio, Fernando Fernández, Daniel Borrajo:
  Machine Learning of Plan Robustness Knowledge About Instances. 609-616
• 
  Arnaud Lallouet, Andrei Legtchenko:
  Two Contributions of Constraint Programming to Machine Learning. 617-624
• 
  Tao Li, Wei Peng:
  A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files. 625-632
• 
  Fletcher Lu, J. Efrim Boritz:
  Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions. 633-640
• 
  Ingo Mierswa, Michael Wurst:
  Efficient Case Based Feature Construction. 641-648
• 
  Richard Nock, Frank Nielsen:
  Fitting the Smallest Enclosing Bregman Ball. 649-656
• 
  Daniel Oblinger, Vittorio Castelli, Tessa A. Lau, Lawrence D. Bergman:
  Similarity-Based Alignment and Generalization. 657-664
• 
  Oleg Okun, Helen Priisalu, Alexessander Alves:
  Fast Non-negative Dimensionality Reduction for Protein Fold Recognition. 665-672
• 
  Irene M. Ong, Inês de Castro Dutra, David Page, Vítor Santos Costa:
  Mode Directed Path Finding. 673-681
• 
  Xuan Hieu Phan, Minh Le Nguyen, Susumu Horiguchi, Tu Bao Ho, Yasushi Inoguchi:
  Classification with Maximum Entropy Modeling of Predictive Association Rules. 682-689
• 
  Joaquim F. Pinto da Costa, Jaime S. Cardoso:
  Classification of Ordinal Data Using Neural Networks. 690-697
• 
  Barnabás Póczos, Bálint Takács, András Lörincz:
  Independent Subspace Analysis on Innovations. 698-706
• 
  Ricardo Rocha, Nuno A. Fonseca, Vítor Santos Costa:
  On Applying Tabling to Inductive Logic Programming. 707-714
• 
  (Withdrawn) Learning Models of Relational Stochastic Processes. 715-723
• 
  Surendra K. Singhi, Huan Liu:
  Error-Sensitive Grading for Model Combination. 724-732
• 
  Hendrik Skubch, Michael Thielscher:
  Strategy Learning for Reasoning Agents. 733-740
• 
  Yuk Lai Suen, Prem Melville, Raymond J. Mooney:
  Combining Bias and Variance Reduction Techniques for Regression Trees. 741-749
• 
  Petroula Tsampouka, John Shawe-Taylor:
  Analysis of Generic Perceptron-Like Large Margin Classifiers. 750-758
• 
  Fang Wang, Yuhui Qiu:
  Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm. 759-766

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last updated on 2025-12-12 01:07 CET by the dblp team

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