Soft Computing in Information Retrieval

Techniques and Applications

By: Fabio Crestani (Editor), Gabriella Pasi (Editor)

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Hardcover | 26 May 2000

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412 Pages

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23.5 x 15.88 x 1.91

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Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. The aim of an IR system is to estimate the relevance of documents to users' information needs, expressed by means of a query. This is a very difficult and complex task, since it is pervaded with imprecision and uncertainty. Most of the existing IR systems offer a very simple model of IR, which privileges efficiency at the expense of effectiveness. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" 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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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Information Retrieval Computer Science Artificial Intelligence Library & Info Sciences Automation of Library & Information Services Reference, Information & Interdisciplinary Subjects Research & Information Information theory Business & Management

Business Mathematics & Systems

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Paperback

Published: 21st October 2010

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Foreword
Preface
A Framework for Linguistic and Hierarchical Queries in Document Retrieval	p. 3
Application of Fuzzy Set Theory to Extend Boolean Information Retrieval	p. 21
A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words	p. 48
Visual Keywords: from Text Retrieval to Multimedia Retrieval	p. 77
Document Classification with Unsupervised Artificial Neural Networks	p. 102
The Java Search Agent Workshop	p. 122
A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes	p. 141
Connectionist and Genetic Approaches for Information Retrieval	p. 173
Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval	p. 199
A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory	p. 225
Bayesian Network Models for Information Retrieval	p. 259
Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance	p. 292
Granular Information Retrieval	p. 317
A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics	p. 332
Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval	p. 358
Rough Sets and Multisets in a Model of Information Retrieval	p. 373
Table of Contents provided by Blackwell. All Rights Reserved.

Soft Computing in Information Retrieval

Techniques and Applications

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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