Learning and Soft Computing

Support Vector Machines, Neural Networks, and Fuzzy Logic Models

by Kecman

ISBN: 9780262333078 | Copyright 2001

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Contents (pg. vii)
Preface (pg. xi)
Introduction (pg. xvii)
1 Learning and Soft Computing (pg. 1)
1.1 Examples of Applications in Diverse Fields (pg. 2)
1.2 Basic Tools of Soft Computing: Neural Networks, Fuzzy Logic Systems, and Support Vector Machines (pg. 9)
1.3 Basic Mathematics of Soft Computing (pg. 24)
1.4 Learning and Statistical Approaches to Regression and Classification (pg. 61)
2 Support Vector Machines (pg. 121)
2.1 Risk Minimization Principles and the Concept of Uniform Convergence (pg. 129)
2.2 The VC Dimension (pg. 138)
2.3 Structual Risk Minimization (pg. 145)
2.4 Support Vector Machine Algorithms (pg. 148)
3 Single-Layer Networks (pg. 193)
3.1 The Perceptron (pg. 194)
3.2 The Adaptive Linear Neuron (Adaline) and the Least Mean Square Algorithm (pg. 213)
4 Multilayer Perceptrons (pg. 255)
4.1 The Error Backpropagation Algorithm (pg. 255)
4.2 The Generalized Delta Rule (pg. 260)
4.3 Heuristics or Practical Aspects of the Error Backpropagation Algorithm (pg. 266)
5 Radial Basis Function Networks (pg. 313)
5.1 Ill-Posed Problems and the Regularization Technique (pg. 314)
5.2 Stabilizers and Basis Functions (pg. 329)
5.3 Generalized Radial Basis Function Networks (pg. 333)
6 Fuzzy Logic Systems (pg. 365)
6.1 Basics of Fuzzy Logic Theory (pg. 367)
6.2 Mathematical Similarities Between Neural Networks and Fuzzy Logic Models (pg. 396)
6.3 Fuzzy Additive Models (pg. 404)
7 Case Studies (pg. 421)
7.1 Neural Networks-Based Adaptive Control (pg. 421)
7.2 Financial Time Series Analysis (pg. 449)
7.3 Computer Graphics (pg. 463)
8 Basic Nonlinear Optimization Methods (pg. 481)
8.1 Classical Methods (pg. 482)
8.2 Genetic Algorithms and Evolutionary Computing (pg. 496)
9 Mathematical Tools of Soft Computing (pg. 505)
9.1 Systems of Linear Equations (pg. 505)
9.2 Vectors and Matrices (pg. 510)
9.3 Linear Algebra and Analytic Geometry (pg. 516)
9.4 Basics of Multivariable Analysis (pg. 518)
9.5 Basics from Probability Theory (pg. 520)
Selected Abbreviations (pg. 525)
Notes (pg. 527)
Preface (pg. 527)
Chapter 1 (pg. 527)
Chapter 2 (pg. 528)
Chapter 3 (pg. 528)
Chapter 4 (pg. 529)
Chapter 5 (pg. 529)
Chapter 6 (pg. 529)
Chapter 7 (pg. 530)
Chapter 9 (pg. 530)
References (pg. 531)
Index (pg. 539)

Vojislav Kecman

Vojislav Kecman is Professor in the School of Engineering at Virginia Commonwealth University.


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