Classification of Individuals with Complex Structure

Bowers A. F., Giraud-Carrier C., Lloyd J. W.

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

This paper introduces a foundation for inductive learning based on the use of higher-order logic for knowledge representation. In particular, the paper (i) provides a systematic individuals-as-terms approach to knowledge representation for inductive learning, and demonstrates the utility of types and higher-order constructs for this purpose; (ii) introduces a systematic way to construct predicates for use in induced definitions; and (iii) widens the applicability of decision-tree algorithms beyond the usual attribute-value setting to the classification of individuals with complex internal structure. The paper contains several illustrative applications. The effectiveness of the approach is demonstrated by applying the decision-tree learning system to two benchmark problems.
Translated title of the contributionClassification of Individuals with Complex Structure
Original languageEnglish
Pages (from-to)81-88
JournalProceedings of the Seventeenth International Conference on Machine Learning (ICML'2000)
Publication statusPublished - 2000

Bibliographical note

ISBN: 1558607072
Publisher: Morgan Kaufmann
Name and Venue of Conference: Proceedings of the Seventeenth International Conference on Machine Learning (ICML'2000)
Other identifier: 1000481

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