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Soft Computing in Information Retrieval

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Cover of 'Soft Computing in Information Retrieval'

Table of Contents

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    Book Overview
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    Chapter 1 A Framework for Linguistic and Hierarchical Queries in Document Retrieval
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    Chapter 2 Application of Fuzzy Set Theory to Extend Boolean Information Retrieval
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    Chapter 3 A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words
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    Chapter 4 Visual Keywords: from Text Retrieval to Multimedia Retrieval
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    Chapter 5 Document Classification with Unsupervised Artificial Neural Networks
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    Chapter 6 The Java Search Agent Workshop
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    Chapter 7 A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes
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    Chapter 8 Connectionist and Genetic Approaches for Information Retrieval
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    Chapter 9 Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval
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    Chapter 10 A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory
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    Chapter 11 Bayesian Network Models for Information Retrieval
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    Chapter 12 Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance
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    Chapter 13 Granular Information Retrieval
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    Chapter 14 A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics
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    Chapter 15 Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval
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    Chapter 16 Rough Sets and Multisets in a Model of Information Retrieval
Overall attention for this book and its chapters
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8 Mendeley
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Title
Soft Computing in Information Retrieval
Published by
Physica-Verlag HD, March 2013
DOI 10.1007/978-3-7908-1849-9
ISBNs
978-3-79-082473-5, 978-3-79-081849-9
Editors

Crestani, Fabio, Pasi, Gabriella

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 25%
Professor 1 13%
Lecturer 1 13%
Student > Bachelor 1 13%
Other 1 13%
Other 2 25%
Readers by discipline Count As %
Engineering 5 63%
Unspecified 1 13%
Computer Science 1 13%
Design 1 13%

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