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Pattern classification : neuro-fuzzy methods and their comparison

Pattern classification : neuro-fuzzy methods and their comparison

Material type
단행본
Personal Author
Abe, Shigeo.
Title Statement
Pattern classification : neuro-fuzzy methods and their comparison / Shigeo Abe.
Publication, Distribution, etc
London ;   New York :   Springer,   c2001.  
Physical Medium
xix, 327 p. : ill. ; 24 cm.
ISBN
1852333529 (alk. paper)
Bibliography, Etc. Note
Includes bibliographical references (p. [315]-321) and index.
Subject Added Entry-Topical Term
Neural networks (Computer science) Fuzzy systems. Pattern recognition systems.
000 00842camuu2200253 a 4500
001 000001070646
005 20020417102830
008 000606s2001 enka b 001 0 eng
020 ▼a 1852333529 (alk. paper)
040 ▼a DLC ▼c DLC ▼d DLC ▼d 244002
042 ▼a pcc
049 0 ▼l 151123313
050 0 0 ▼a QA76.87. ▼b A323 2001
082 0 0 ▼a 006.3/2 ▼2 21
090 ▼a 006.32 ▼b A138p
100 1 ▼a Abe, Shigeo.
245 1 0 ▼a Pattern classification : ▼b neuro-fuzzy methods and their comparison / ▼c Shigeo Abe.
260 ▼a London ; ▼a New York : ▼b Springer, ▼c c2001.
300 ▼a xix, 327 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references (p. [315]-321) and index.
650 0 ▼a Neural networks (Computer science)
650 0 ▼a Fuzzy systems.
650 0 ▼a Pattern recognition systems.

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Sejong Academic Information Center/Science & Technology/ Call Number 006.32 A138p Accession No. 151123313 Availability Available Due Date Make a Reservation Service C

Contents information

Table of Contents

I. Pattern Classification.- 1. Introduction.- 1.1 Development of a Classification System.- 1.2 Optimum Features.- 1.3 Classifiers.- 1.3.1 Neural Network Classifiers.- 1.3.2 Conventional Fuzzy Classifiers.- 1.3.3 Fuzzy Classifiers with Learning Capability.- 1.4 Evaluation.- 1.5 Data Sets Used in the Book.- 2. Multilayer Neural Network Classifiers.- 2.1 Three-layer Neural Networks.- 2.2 Synthesis Principles.- 2.3 Training Methods.- 2.4 Training by the Back-propagation Algorithm.- 2.5 Training by Solving Inequalities.- 2.5.1 Setting of Target Values.- 2.5.2 Formulation of Training by Solving Inequalities.- 2.5.3 Determination of Weights by Solving Inequalities.- 2.6 Performance Evaluation.- 2.6.1 Iris Data.- 2.6.2 Numeral Data.- 2.6.3 Thyroid Data.- 2.6.4 Blood Cell Data.- 2.6.5 Hiragana Data.- 2.6.6 Discussions.- 3. Support Vector Machines.- 3.1 Support Vector Machines for Pattern Classification.- 3.1.1 Conversion to Two-class Problems.- 3.1.2 The Optimal Hyperplane.- 3.1.3 Mapping to a High-dimensional Space.- 3.2 Performance Evaluation.- 3.2.1 Iris Data.- 3.2.2 Numeral Data.- 3.2.3 Thyroid Data.- 3.2.4 Blood Cell Data.- 3.2.5 Hiragana Data.- 3.2.6 Discussions.- 4. Membership Functions.- 4.1 One-dimensional Membership Functions.- 4.1.1 Triangular Membership Functions.- 4.1.2 Trapezoidal Membership Functions.- 4.1.3 Bell-shaped Membership Functions.- 4.2 Multi-dimensional Membership Functions.- 4.2.1 Extension to Multi-dimensional Membership Functions.- 4.2.2 Rectangular Pyramidal Membership Functions.- 4.2.3 Truncated Rectangular Pyramidal Membership Functions.- 4.2.4 Polyhedral Pyramidal Membership Functions.- 4.2.5 Truncated Polyhedral Pyramidal Membership Functions.- 4.2.6 Bell-shaped Membership Functions.- 4.2.7 Relations between Membership Functions.- 5. Static Fuzzy Rule Generation.- 5.1 Classifier Architecture.- 5.2 Fuzzy Rules.- 5.2.1 Fuzzy Rules with Pyramidal Membership Functions.- 5.2.2 Polyhedral Fuzzy Rules.- 5.2.3 Ellipsoidal Fuzzy Rules.- 5.3 Class Boundaries.- 5.3.1 Fuzzy Rules with Pyramidal Membership Functions.- 5.3.2 Ellipsoidal Fuzzy Rules.- 5.3.3 Class Boundaries for the Iris Data.- 5.4 Training Architecture.- 5.4.1 Fuzzy Rule Generation by Preclustering.- 5.4.2 Fuzzy Rule Generation by Postclustering.- 6. Clustering.- 6.1 Fuzzy c-means Clustering Algorithm.- 6.2 The Kohonen Network.- 6.3 Minimum Volume Clustering Algorithm.- 6.4 Fuzzy Min-max Clustering Algorithm.- 6.5 Overlap Resolving Clustering Algorithm.- 6.5.1 Approximation of Overlapping Regions.- 6.5.2 Extraction of Data from the Overlapping Regions.- 6.5.3 Clustering Algorithm.- 7. Tuning of Membership Functions.- 7.1 Problem Formulation.- 7.2 Direct Methods.- 7.2.1 Tuning of Slopes.- 7.2.2 Tuning of Locations.- 7.2.3 Order of Tuning.- 7.3 Indirect Methods.- 7.3.1 Tuning of Slopes Using the Least-squares Method.- 7.3.2 Tuning by the Steepest Descent Method.- 7.4 Performance Evaluation.- 7.4.1 Performance Evaluation of the Fuzzy Classifier with Pyramidal Membership Functions.- 7.4.2 Performance Evaluation of the Fuzzy Classifier with Polyhedral Regions.- 7.4.3 Performance Evaluation of the Fuzzy Classifier with Ellipsoidal Regions.- 8. Robust Pattern Classification.- 8.1 Why Robust Classification Is Necessary?.- 8.2 Robust Classification.- 8.2.1 The First Stage.- 8.2.2 The Second Stage.- 8.2.3 Tuning Slopes near Class Boundaries.- 8.2.4 Upper and Lower Bounds Determined by Correctly Classified Data.- 8.2.5 Range of the Interclass Tuning Parameter that Resolves Misclassification.- 8.3 Performance Evaluation.- 8.3.1 Classification Performance without Outliers.- 8.3.2 Classification Performance with Outliers.- 9. Dynamic Fuzzy Rule Generation.- 9.1 Fuzzy Min-max Classifiers.- 9.1.1 Concept.- 9.1.2 Approximation of Input Regions.- 9.1.3 Fuzzy Rule Extraction.- 9.1.4 Performance Evaluation.- 9.2 Fuzzy Min-max Classifiers with Inhibition.- 9.2.1 Concept.- 9.2.2 Fuzzy Rule Extraction.- 9.2.3 Fuzzy Rule Inference.- 9.2.4 Performance Evaluation.- 10. Co


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