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Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings de Maarten van Someren

Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings: Lecture Notes in Computer Science, cartea 1224

Editat de Maarten van Someren, Gerhard Widmer
en Limba Engleză Paperback – 9 apr 1997
This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997.
This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.
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Specificații

ISBN-13: 9783540628583
ISBN-10: 3540628584
Pagini: 380
Ilustrații: XIV, 366 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.53 kg
Ediția:1997
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.