Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings
Nada Lavrač, Dragan Gamberger, Ljupco Todorovski, Hendrik Blockeel
Springer, Nov 5, 2003 - Machine learning - 504 pages
This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.
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accuracy actions ADABOOST agent approach approximation Artificial Intelligence attributes automata Bayesian Bayesian networks boosting CGEM class label classifier clustering complexity Computer concept constraints context convergence corpus cost cross-validation datasets decision tree defined denote distribution documents domain ECML ensemble error evaluation event experiments exploration feature FrameNet function given graph grid improve inference input instances iterations kernel Kernel PCA learner learning algorithms linear LNAI logistic regression Machine Learning matrix measure merging method multi-instance Naive Bayes Nash equilibrium networks Neural node optimal Oracle pairwise paper parameters parse performance players prediction probabilistic problem Proceedings proposed pruning Q-learning Qfilter qualitative random ranking regression reinforcement learning Replicator Dynamics role rule sample search engines Section selection space split statistical strategy subset subtrees Support Vector Machines Table task techniques threshold tion topic training data training set translation treebank variables vector weight word