Categorizing Web documents using competitive learning: an ingredient of a personal adaptive agent

This paper describes the application of competitive learning to categorize Web documents, which is one component of our adaptive Web agent. The agent learns the different categories of Web documents that the user is interested in, then finds and suggests new similar documents. We discuss the framework of the Web agent, the implementation of the document clustering and current results. This work therefore suggests a new application, rather than an improved learning algorithm.