内容

目次

Learning in agent-oriented worlds.- Naive (Bayes) at forty: The independence assumption in information retrieval.- Learning verbal transitivity using loglinear models.- Part-of-speech tagging using decision trees.- Inference of finite automata: Reducing the search space with an ordering of pairs of states.- Automatic acquisition of lexical knowledge from sparse and noisy data.- A normalization method for contextual data: Experience from a large-scale application.- Learning to classify x-ray images using relational learning.- ILP experiments in detecting traffic problems.- Simulating children learning and explaining elementary heat transfer phenomena: A multistrategy system at work.- Bayes optimal instance-based learning.- Bayesian and information-theoretic priors for Bayesian network parameters.- Feature subset selection in text-learning.- A monotonic measure for optimal feature selection.- Inducing models of human control skills.- God doesn't always shave with Occam's razor — Learning when and how to prune.- Error estimators for pruning regression trees.- Pruning decision trees with misclassification costs.- Text categorization with Support Vector Machines: Learning with many relevant features.- A short note about the application of polynomial kernels with fractional degree in Support Vector Learning.- Classification learning using all rules.- Improved pairwise coupling classification with correcting classifiers.- Experiments on solving multiclass learning problems by n 2-classifier.- Combining classifiers by constructive induction.- Boosting trees for cost-sensitive classifications.- Naive bayesian classifier committees.- Batch classifications with discrete finite mixtures.- Induction of recursive program schemes.- Predicate invention and learning from positive examples only.- An inductive logic programming framework to learn a concept from ambiguous examples.- First-order learning for Web mining.- Explanation-based generalization in game playing: Quantitative results.- Scope classification: An instance-based learning algorithm with a rule-based characterisation.- Error-correcting output codes for local learners.- Recursive lazy learning for modeling and control.- Using lattice-based framework as a tool for feature extraction.- Determining property relevance in concept formation by computing correlation between properties.- A buffering strategy to avoid ordering effects in clustering.- Coevolutionary, distributed search for inducing concept descriptions.- Continuous mimetic evolution.- A host-parasite genetic algorithm for asymmetric tasks.- Speeding up Q(?)-learning.- Q-learning and redundancy reduction in classifier systems with internal state.- Composing functions to speed up reinforcement learning in a changing world.- Theoretical results on reinforcement learning with temporally abstract options.- A general convergence method for Reinforcement Learning in the continuous case.- Interpretable neural networks with BP-SOM.- Convergence rate of minimization learning for neural networks.