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Proceedings Paper

Training perceptrons for document search over the World Wide Web
Author(s): Zhixiang Chen; Xiannong Meng; Richard K. Fox; Richard H. Fowler
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Paper Abstract

In this paper we study the problem of searching documents over the world wide web through training perceptrons. We consider that web documents can be represented by vectors of n boolean attributes. A search process can be viewed as a way of classifying documents over the web according to the user's requirements. We design a perceptron training algorithm for the search engine, and give a bound on the number of trails needed to search for any collection of documents represented by a disjunction of the relevant boolean attributes.

Paper Details

Date Published: 1 November 1999
PDF: 5 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367706
Show Author Affiliations
Zhixiang Chen, Univ. of Texas/Pan American (United States)
Xiannong Meng, Univ. of Texas/Pan American (United States)
Richard K. Fox, Univ. of Texas/Pan American (United States)
Richard H. Fowler, Univ. of Texas/Pan American (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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