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Computational Intelligence in Theory and Practice

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Cover of 'Computational Intelligence in Theory and Practice'

Table of Contents

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    Book Overview
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    Chapter 1 A Fuzzy Set Approach to Case-Based Decision
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    Chapter 2 Proof Theory of Many-Valued Logic and Linear Optimization
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    Chapter 3 Triangular Norms — An Overview
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    Chapter 4 A First View on the Alternatives of Fuzzy Set Theory
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    Chapter 5 Rough Sets and their Applications
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    Chapter 6 Generalizing the Explicit Concept of Rough Set on the Basis of Modal Logic
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    Chapter 7 Probalistic Networks and Fuzzy Clustering as Generalizations of Naive Bayes Classifiers
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    Chapter 8 Granular Computing in Fuzzy Modeling and Data Mining
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    Chapter 9 Evolutionary Computation and Mathematical Programming
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    Chapter 10 Genetic Optimization of Fuzzy Classification Systems — A Case Study
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    Chapter 11 Graduality and Databases: Some Contributions of Fuzzy Sets
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    Chapter 12 Fuzzy Retrieval of Object-Oriented Software Components
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    Chapter 13 Using Fuzzy Quering over the Internet to Browse through Information Resources
Attention for Chapter 2: Proof Theory of Many-Valued Logic and Linear Optimization
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Chapter title
Proof Theory of Many-Valued Logic and Linear Optimization
Chapter number 2
Book title
Computational Intelligence in Theory and Practice
Published by
Physica, Heidelberg, January 2001
DOI 10.1007/978-3-7908-1831-4_2
Book ISBNs
978-3-79-081357-9, 978-3-79-081831-4
Authors

Reiner Hähnle, Hähnle, Reiner

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