Training perceptrons for document search over the World Wide Web

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.