Machine Learning: ECML'97 : 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings / edited by Maarten van Someren, Gerhard Widmer.

Format:

Book

Contributor:

Someren, Maarten van, editor.

Widmer, Gerhard, 1961- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1224.

Lecture Notes in Artificial Intelligence ; 1224

Language:

English

Subjects (All):

Artificial intelligence.

Algorithms.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Local Subjects:

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Physical Description:

1 online resource (XIV, 366 pages).

Edition:

First edition 1997.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.

System Details:

text file PDF

Summary:

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.

Contents:

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 and distributed 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.

Other Format:

Printed edition:

ISBN:

978-3-540-68708-5

9783540687085

Access Restriction:

Restricted for use by site license.