Concept-Learning In The Absence Of Counter-Examples: An Autoassociation-Based Approach To Classification (1999)
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BibTeX
@MISC{Japkowicz99concept-learningin,
author = {Nathalie Japkowicz},
title = {Concept-Learning In The Absence Of Counter-Examples: An Autoassociation-Based Approach To Classification},
year = {1999}
}
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Abstract
The overwhelming majority of research currently pursued within the framework of concept-learning concentrates on discrimination-based learning, an inductive learning paradigm that relies on both examples and counter-examples of the concept. This emphasis, however, can present a practical problem: there are real-world engineering problems for which counter-examples are both scarce and difficult to gather. For these problems, recognition-based learning systems are much more appropriate because they do not use counter-examples in the conceptlearning phase. The purpose of this dissertation is to analyze a connectionist recognition-based learning system---autoassociation-based classification---and answer the following questions: ffl What features of the autoassociator make it ca...
