Advertisement

Soft Computing in Information Retrieval

Techniques and Applications

  • Fabio Crestani
  • Gabriella Pasi

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 50)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Fuzzy Set Theory

  3. Neural Networks

    1. Front Matter
      Pages 75-75
    2. Hsinchun Chen, Marshall Ramsey, Po Li
      Pages 122-140
  4. Genetic Algorithms

    1. Front Matter
      Pages 171-171
    2. Mohand Boughanem, Claude Chrisment, Josiane Mothe, Chantal Soule-Dupuy, Lynda Tamine
      Pages 173-198
  5. Evidential and Probabilistic Reasoning

    1. Front Matter
      Pages 223-223
    2. Berthier Ribeiro-Neto, Ilmério Silva, Richard Muntz
      Pages 259-291
  6. Rough Sets Theory, Multivalued Logics, and Other Approaches

    1. Front Matter
      Pages 315-315
    2. S. K. Michael Wong, Y. Y. Yao, Cory J. Butz
      Pages 317-331
    3. Padmini Srinivasan, Donald Kraft, Jianhua Chen
      Pages 358-372
  7. Back Matter
    Pages 395-395

About this book

Introduction

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.

Keywords

Bayesian network algorithms classification data mining fuzzy fuzzy sets genetic algorithms information information retrieval learning multimedia networks neural networks probabilistic reasoning uncertainty

Editors and affiliations

  • Fabio Crestani
    • 1
  • Gabriella Pasi
    • 2
  1. 1.Department of Computing ScienceUniversity of GlasgowGlasgowScotland
  2. 2.ITIM-CNRMilanoItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7908-1849-9
  • Copyright Information Physica-Verlag Heidelberg 2000
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2473-5
  • Online ISBN 978-3-7908-1849-9
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site