Abstract
A neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the EαNet architecture, a neural network having good generalization capabilities and able to learn the activation function of its hidden units. 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 is composed by three interacting agents: the EαNet Neural Classifier Mobile Agent, the Query Agent, and the Locator Agent. The whole system was successfully implemented exploiting the Jade platform features and facilities. The preliminary experimental results show a good classification rate: in the best case a classification error of 9.98% is reached.
This research has been partially funded by Engineering Ingegneria Informatica SpA within the Teschet project.
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Pilato, G., Vitabile, S., Vassallo, G., Conti, V., Sorbello, F. (2003). A Concurrent Neural Classifier for HTML Documents Retrieval. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_24
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DOI: https://doi.org/10.1007/978-3-540-45216-4_24
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