6533b86efe1ef96bd12cbc95
RESEARCH PRODUCT
A Concurrent Neural Classifier for HTML Documents Retrieval
Giorgio VassalloGiovanni PilatoSalvatore VitabileF SorbelloF SorbelloVincenzo Contisubject
Network architectureArtificial neural networkComputer sciencebusiness.industryActivation functionHTMLMachine learningcomputer.software_genreMobile agentArtificial intelligenceDocument retrievalbusinesscomputerClassifier (UML)computer.programming_languagedescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2003-01-01 |