A Concurrent Neural Classifier for HTML Documents Retrieval

A neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the E-alpha-Net architecture, a neural network able to learn the activation function of its hidden units and having good generalization capabilities. The starting hypothesis is that the HTML pages are stored in networked repositories. The system goal is to retrieve documents satisfying a user query and belonging to a given class (i.e. documents containing the word “football” and talking about “Sports”). The system has been implemented using the Jade platform and it is composed by three agents: the E-alpha-Net Neural Classifier Agent, the Query Agent, and the Locator Agent. The system is very efficient: the preliminary experimental results show that in the best case a classification error of 9.98% is obtained.