
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. |
소장정보
No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
---|---|---|---|---|---|---|---|
No. 1 | 소장처 세종학술정보원/과학기술실/ | 청구기호 006.32 A138p | 등록번호 151123313 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
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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