Learning and Soft Computing
Support Vector Machines, Neural Networks, and Fuzzy Logic Models
by Kecman
ISBN: 9780262527903 | Copyright 2001
Instructor Requests
| Expand/Collapse All | |
|---|---|
| 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.
| Instructors Only | |
|---|---|
|
You must have an instructor account and submit a request to access instructor materials for this book.
|
|
eTextbook
Go paperless today! Available online anytime, nothing to download or install.
Features
|
