Inductive Logic Programming : 12th International Conference, ILP 2002, Sydney, Australia, July 9-11, 2002. Revised Papers / edited by Stan Matwin, Claude Sammut.

Format:

Book

Contributor:

Matwin, Stan, editor.

Sammut, Claude, 1956- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2583.

Lecture Notes in Artificial Intelligence ; 2583

Language:

English

Subjects (All):

Software engineering.

Artificial intelligence.

Computer science.

Computer programming.

Algorithms.

Logic, Symbolic and mathematical.

Software Engineering/Programming and Operating Systems.

Artificial Intelligence.

Computer Science, general.

Programming Techniques.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Local Subjects:

Software Engineering/Programming and Operating Systems.

Artificial Intelligence.

Computer Science, general.

Programming Techniques.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (X, 358 pages).

Edition:

First edition 2003.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.

System Details:

text file PDF

Summary:

The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9-11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the ?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept.

Contents:

Contributed Papers

Propositionalization for Clustering Symbolic Relational Descriptions

Efficient and Effective Induction of First Order Decision Lists

Learning with Feature Description Logics

An Empirical Evaluation of Bagging in Inductive Logic Programming

Kernels for Structured Data

Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems

Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners

Learnability of Description Logic Programs

1BC2: A True First-Order Bayesian Classifier

RSD: Relational Subgroup Discovery through First-Order Feature Construction

Mining Frequent Logical Sequences with SPIRIT-LoG

Using Theory Completion to Learn a Robot Navigation Control Program

Learning Structure and Parameters of Stochastic Logic Programs

A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars

Revision of First-Order Bayesian Classifiers

The Applicability to ILP of Results Concerning the Ordering of Binomial Populations

Compact Representation of Knowledge Bases in ILP

A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data

A Genetic Algorithms Approach to ILP

Experimental Investigation of Pruning Methods for Relational Pattern Discovery

Noise-Resistant Incremental Relational Learning Using Possible Worlds

Lattice-Search Runtime Distributions May Be Heavy-Tailed

Invited Talk Abstracts

Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery.

Other Format:

Printed edition:

ISBN:

978-3-540-36468-9

9783540364689

Access Restriction:

Restricted for use by site license.