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.
A Concurrent Neural Classifier for HTML Documents Retrieval
Pilato Giovanni;Vitabile Salvatore
2003
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
