Machine Learning: ECML'97

Uncertain learning agents.- Constructing and sharing perceptual distinctions.- On prediction by data compression.- Induction of feature terms with INDIE.- Exploiting qualitative knowledge to enhance skill acquisition.- Integrated learning and planning based on truncating temporal differences.- ?-subsumption for structural matching.- Classification by Voting Feature Intervals.- Constructing intermediate concepts by decomposition of real functions.- Conditions for Occam's razor applicability and noise elimination.- Learning different types of new attributes by combining the neural network and iterative attribute construction.- Metrics on terms and clauses.- Learning when negative examples abound.- A model for generalization based on confirmatory induction.- Learning Linear Constraints in Inductive Logic Programming.- Finite-Element methods with local triangulation refinement for continuous reinforcement learning problems.- Inductive Genetic Programming with Decision Trees.- Parallel anddistributed search for structure in multivariate time series.- Compression-based pruning of decision lists.- Probabilistic Incremental Program Evolution: Stochastic search through program space.- NeuroLinear: A system for extracting oblique decision rules from neural networks.- Inducing and using decision rules in the GRG knowledge discovery system.- Learning and exploitation do not conflict under minimax optimality.- Model combination in the multiple-data-batches scenario.- Search-based class discretization.- Natural ideal operators in Inductive Logic Programming.- A case study in loyalty and satisfaction research.- Ibots learn genuine team solutions.- Global data analysis and the fragmentation problem in decision tree induction.- Case-based learning: Beyond classification of feature vectors.- Empirical learning of Natural Language Processing tasks.- Human-Agent Interaction and Machine Learning.- Learning in dynamically changing domains: Theory revision and context dependence issues.