Training perceptrons for document search over the World Wide Web

Zhixiang Chen; Xiannong Meng; Richard K. Fox; Richard H. Fowler

doi:10.1117/12.367706

1 November 1999 Training perceptrons for document search over the World Wide Web

Zhixiang Chen, Xiannong Meng, Richard K. Fox, Richard H. Fowler

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367706
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

Citation Download Citation

Zhixiang Chen, Xiannong Meng, Richard K. Fox, and Richard H. Fowler "Training perceptrons for document search over the World Wide Web", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367706

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