NDLI: Choosing the initial set of exemplars when learning with an NGE-based system

Content Provider	IEEE Xplore Digital Library
Author	Figueira, L.B. do Carmo Nicoletti, M.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Univ. Fed. de Sao Carlos, Brazil (Figueira, L.B.; do Carmo Nicoletti, M.)
Abstract	In the original proposal of the NGE (nested generalized exemplar) system, the induction of a concept is based on an initial set of training examples (named seeds) that are randomly chosen. The number of examples in this set is arbitrary, generally determined by the user of the system. It can be seen empirically, that the final results are influenced by the initial choice of the seeds. We propose and investigate other alternative methods for choosing seeds and empirically evaluate their impact on the learning results in seven knowledge domains, as far as accuracy and number of expressions describing the concepts are concerned. In spite of the additional time investment associated with using a clustering method and, assuming that accuracy of the induced concept is of major importance, experiments have shown that choosing the initial set of seeds as the center of clusters can be the best option.
Sponsorship	IEEE Comput. Soc. Task Force on Information Technology for Business Application
Starting Page	193
Ending Page	197
File Size	1342324
Page Count	5
File Format	PDF
ISBN	0769521088
DOI	10.1109/ITCC.2004.1286630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proposals Machine learning Computer science Nearest neighbor searches Neural networks Euclidean distance Investments Clustering methods Machine learning algorithms Information technology
Content Type	Text
Resource Type	Article

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