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Soft Computing in Web Information Retrieval

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Cover of 'Soft Computing in Web Information Retrieval'

Table of Contents

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    Book Overview
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    Chapter 1 A Dynamic Hierarchical Fuzzy Clustering Algorithm for Information Filtering
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    Chapter 2 A Theoretical Framework for Web Categorization in Hierarchical Directories using Bayesian Networks
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    Chapter 3 Personalized Knowledge Models Using RDF-Based Fuzzy Classification
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    Chapter 4 A Genetic Programming Approach for Combining Structural and Citation-Based Evidence for Text Classification in Web Digital Libraries
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    Chapter 5 Adding a Trust Layer to Semantic Web Metadata
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    Chapter 6 A Fuzzy Linguistic Multi-agent Model Based on Semantic Web Technologies and User Profiles
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    Chapter 7 Fuzzy Concept-Based Models in Information Browsers
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    Chapter 8 Evaluation of Term-based Queries using Possibilistic Ontologies
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    Chapter 9 Formal Theory of Connectionist Web Retrieval
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    Chapter 10 Semi-fuzzy Quantifiers for Information Retrieval
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    Chapter 11 Helping Users in Web Information Retrieval Via Fuzzy Association Rules
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    Chapter 12 Combining Soft and Hard Techniques for the Analysis of Batch Retrieval Tasks
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    Chapter 13 Search Advertising
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    Chapter 14 Information Loss in Continuous Hybrid Microdata: Subdomain-Level Probabilistic Measures
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    Chapter 15 Access to a Large Dictionary of Spanish Synonyms: A Tool for Fuzzy Information Retrieval
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Citations

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Title
Soft Computing in Web Information Retrieval
Published by
Springer Berlin Heidelberg, January 2006
DOI 10.1007/3-540-31590-x
ISBNs
978-3-54-031588-9, 978-3-54-031590-2
Editors

Professor Enrique Herrera-Viedma, Professor Gabriella Pasi, Professor Fabio Crestani

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 33%
Student > Ph. D. Student 1 33%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Computer Science 3 100%