Titlebook: Intelligent Exploration of the Web; Piotr S. Szczepaniak,Javier Segovia,Lotfi A. Zadeh Book 2003 Springer-Verlag Berlin Heidelberg 2003 We

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Textual Information Retrieval with User Profiles Using Fuzzy Clustering and Inferencingollection of terms extracted from the set of documents for a specific user or a group of users. We use a fuzzy representation for user profiles where each term in a profile is associated with a fuzzy membership value. The construction of user profiles is performed by a combination of fuzzy clusterin

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Documents Clustering Using Tolerance Rough Set Model and Its Application to Information Retrieval problem because of the unstructured form and textual characteristics of documents. As a consequence, the quality of document clustering depends not only on clustering algorithms but also on document representation models. In this work we introduce a tolerance rough set model (TRSM) for representing

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Intelligent Internet-Based Multiagent Systemsn gathering. Internet Based Multiagent Systems in computing are becoming more complex, resilient, flexible, adaptive and ultimately more intelligent than single agent systems could be. LawBot is one of a growing community of Intelligent Internet Based Multiagent systems. LawBot consists of a collect

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Neural Agent for Text Database Discoverythe user query. Identifying the relevant text databases out of many candidates for a given query is called the .. In this paper, we propose a novel approach, a neural approach, to the .. First, we present a neural agent that learns about underlying text databases from the user’s relevance feedback.

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Intelligent Web Agents that Learn to Retrieve and Extract Information constructs a Web agent by accepting user preferences in form of instructions and adapting the agent’s behavior as it encounters new information. Our approach enables . to rapidly build instructable and self-adaptive Web agents for . (.) and . (.) tasks. . uses two neural networks, which provide ada

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Accurately and Reliably Extracting Data from the Web: A Machine Learning Approachr extracting data from web sites and transforming it into a structured data format, such as XML. The resulting data can then be used to build new applications without having to deal with unstructured data. The advantages of our wrapping technology over previous work are the the ability to learn high

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