1 November 1999 Training perceptrons for document search over the World Wide Web
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhixiang Chen, Xiannong Meng, Richard K. Fox, 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); doi: 10.1117/12.367706; https://doi.org/10.1117/12.367706


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