Machine Learning: ECML'97

9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings
1997
ISBN: 978-3-540-62858-3
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 1224, 366 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1170 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Widmer, Gerhard

Someren, Maarten van

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten

Inhaltsverzeichnis

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.