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Intelligent Text Categorization and Clustering

  • Editors
  • Nadia Nedjah
  • Luiza de Macedo Mourelle
  • Janusz Kacprzyk
  • Felipe M. G. França
  • Alberto Ferreira de De Souza

Part of the Studies in Computational Intelligence book series (SCI, volume 164)

Table of contents

  1. Front Matter
  2. Helyane Bronoski Borges, Julio Cesar Nievola
    Pages 1-23
  3. Bing Quan Huang, Y. B. Zhang, M. -T. Kechadi
    Pages 25-45
  4. Luiz G. P. Almeida, Ana T. R. Vasconcelos, Marco A. G. Maia
    Pages 47-64
  5. Nuttanart Facundes, Ratchaneekorn Theva-aksorn, Booncharoen Sirinaovakul
    Pages 65-79
  6. Alzennyr da Silva, Yves Lechevallier, Francisco de Carvalho
    Pages 81-94
  7. Lando M. di Carlantonio, Rosa Maria E. M. da Costa
    Pages 95-117
  8. Back Matter

About this book

Introduction

Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exist (intelligent web search, data mining, law enforcement, etc.) Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing.

This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering. In the following, we give a brief introduction of the chapters that are included in this book.

Keywords

Algebra Bayesian inference Hypertext Spam Text Categorization algorithm algorithms cognition computational intelligence data mining filtering genetic algorithms handwriting recognition intelligence neural networks

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-85644-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-85643-6
  • Online ISBN 978-3-540-85644-3
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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