Learning different types of new attributes by combining the neural network and iterative attribute construction

Yuh-Jyh Hu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Most of the current constructive induction algorithms degrade performance as the target concept becomes larger and more complex in terms of Boolean combinations. Most are only capable of constructing relatively smaller new attributes. Though it is impossible to build a learner to learn any arbitrarily large and complex concept, there are some large and complex concepts that could be represented in a simple relation such as prototypical concepts, e.g., m-of-n, majority, etc. In this paper, we propose a new approach that combines the neural net and iterative attribute construction to learn relatively short but complex Boolean combinations and prototypical structures. We also carried a series of systematic experiments to characterize our approach.

Original languageEnglish
Title of host publicationMachine Learning
Subtitle of host publicationECML-97 - 9th European Conference on Machine Learning, Proceedings
EditorsMaarten van Someren, Gerhard Widmer, Gerhard Widmer
PublisherSpringer Verlag
Pages124-137
Number of pages14
ISBN (Print)3540628584, 9783540628583
DOIs
StatePublished - 1 Jan 1997
Event9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic
Duration: 23 Apr 199725 Apr 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1224
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th European Conference on Machine Learning, ECML 1997
CountryCzech Republic
CityPrague
Period23/04/9725/04/97

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