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Soft Computing for Knowledge Discovery and Data Mining

Overview of attention for book
Cover of 'Soft Computing for Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Soft Computing for Knowledge Discovery and Data Mining
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    Chapter 2 Neural Networks For Data Mining
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    Chapter 3 Improved SOM Labeling Methodology for Data Mining Applications
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    Chapter 4 A Review of evolutionary Algorithms for Data Mining
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    Chapter 5 Genetic Clustering for Data Mining
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    Chapter 6 Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming
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    Chapter 7 evolutionary Design of Code-matrices for Multiclass Problems
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    Chapter 8 The Role of Fuzzy Sets in Data Mining
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    Chapter 9 Support Vector Machines and Fuzzy Systems
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    Chapter 10 KDD in Marketing with Genetic Fuzzy Systems
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    Chapter 11 Knowledge Discovery in a Framework for Modelling with Words
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    Chapter 12 Swarm Intelligence Algorithms for Data Clustering
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    Chapter 13 A Diffusion Framework for Dimensionality Reduction
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    Chapter 14 Data Mining and Agent Technology: a fruitful symbiosis
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    Chapter 15 Approximate Frequent Itemset Mining In the Presence of Random Noise
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    Chapter 16 The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining
Attention for Chapter 9: Support Vector Machines and Fuzzy Systems
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Citations

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5 Mendeley
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Chapter title
Support Vector Machines and Fuzzy Systems
Chapter number 9
Book title
Soft Computing for Knowledge Discovery and Data Mining
Published by
Springer, Boston, MA, January 2008
DOI 10.1007/978-0-387-69935-6_9
Book ISBNs
978-0-387-69934-9, 978-0-387-69935-6
Authors

Yixin Chen, Chen, Yixin

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Lecturer 1 20%
Student > Postgraduate 1 20%
Readers by discipline Count As %
Computer Science 4 80%
Earth and Planetary Sciences 1 20%

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