Titlebook: Soft Computing in Information Retrieval; Techniques and Appli Fabio Crestani,Gabriella Pasi Book 2000 Physica-Verlag Heidelberg 2000 Bayesi

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门窗的侧柱

Bayesian Network Models for Information Retrievalan effective and flexible framework for dealing with information retrieval (IR) in general. Our discussion focus on two Bayesian networks models proposed in the literature namely, the . and the . models. We compare the expressiveness of these two models and show that the belief network model is more

Wallow

织物

Granular Information Retrieval computational complexity, and the diversity of users. An IR system may be designed to be adaptive by allowing the modification of document and query representation. As well, different retrieval methods can be used for different users. The combina-tion of multi-representation of documents and multi-

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A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logicsde-velopment of so-called intelligent multimedia retrieval Systems. In this paper we will present a logic-based framework in which multimedia objects’ medium dependent properties (objects’ low level features) and multimedia objects’ medium independent properties (abstract objects’ features, or objec

deceive

A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical C documents can be retrieved, or a class of matching words established by some sample document collection and then docu-ments matching with words in this latter class can be retrieved. Various methods of search and retrieval will be proposed and illustrated, with the intention of real application in legal document collections.

anagen

Bayesian Network Models for Information Retrievaland the probabilistic models. Further, we show that a belief network can be used to naturally incorporate pieces of evidence from past user sessions which leads to improved retrieval Performance. At the end, for comparative purposes, we review models of reasoning other than the Bayesian networks and characterize a taxonomy for them.

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规章

Book 2000rinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user‘s concept of relevance. To this aim, the application of soft computing techniques can be of help to obtain greater flexibility in IR systems.

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