by Mehrotra, Mehrotra, Mohan, Mohan, Ranka, Ranka
ISBN: 9780262359740 | Copyright 1996
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Contents (pg. vii) | |
Preface (pg. xiii) | |
Chapter 1. Introduction (pg. 1) | |
1.1 History of Neural Networks (pg. 4) | |
1.2 Structure and Function of a Single Neuron (pg. 7) | |
1.3 Neural Net Architectures (pg. 16) | |
1.4 Neural Learning (pg. 22) | |
1.5 What Can Neural Networks Be Used for? (pg. 24) | |
1.6 Evaluation of Networks (pg. 35) | |
1.7 Implementation (pg. 38) | |
1.8 Conclusion (pg. 39) | |
1.9 Exercises (pg. 41) | |
Chapter 2. Supervised Learning: Single-Layer Networks (pg. 43) | |
2.1 Perceptrons (pg. 43) | |
2.2 Linear Separability (pg. 45) | |
2.3 Perceptron Training Algorithm (pg. 46) | |
2.4 Guarantee of Success (pg. 52) | |
2.5 Modifications (pg. 54) | |
2.6 Conclusion (pg. 61) | |
2.7 Exercises (pg. 62) | |
Chapter 3. Supervised Learning: Multilayer Networks I (pg. 65) | |
3.1 Multilevel Discrimination (pg. 66) | |
3.2 Preliminaries (pg. 67) | |
3.3 Backpropagation Algorithm (pg. 70) | |
3.4 Setting the Parameter Values (pg. 79) | |
3.5 Theoretical Results* (pg. 88) | |
3.6 Accelerating the Learning Process (pg. 93) | |
3.7 Applications (pg. 98) | |
3.8 Conclusion (pg. 105) | |
3.9 Exercises (pg. 106) | |
Chapter 4. Supervised Learning: Multilayer Networks II (pg. 111) | |
4.1 Madalines (pg. 111) | |
4.2 Adaptive Multilayer Networks (pg. 116) | |
4.3 Prediction Networks (pg. 136) | |
4.4 Radial Basis Functions (pg. 141) | |
4.5 Polynomial Networks (pg. 149) | |
4.6 Regularization (pg. 153) | |
4.7 Conclusion (pg. 154) | |
4.8 Exercises (pg. 155) | |
Chapter 5. Unsupervised Learning (pg. 157) | |
5.1 Winner-Take-All Networks (pg. 161) | |
5.2 Learning Vector Quantizers (pg. 173) | |
5.3 Counterpropagation Networks (pg. 176) | |
5.4 Adaptive Resonance Theory (pg. 180) | |
5.5 Topologically Organized Networks (pg. 187) | |
5.6 Distance-Based Learning (pg. 201) | |
5.7 Neocognitron (pg. 204) | |
5.8 Principal Component Analysis Networks (pg. 208) | |
5.9 Conclusion (pg. 213) | |
5.10 Exercises (pg. 214) | |
Chapter 6. Associative Models (pg. 217) | |
6.1 Non-iterative Procedures for Association (pg. 219) | |
6.2 Hopfield Networks (pg. 227) | |
6.3 Brain-State-in-a-Box Network (pg. 244) | |
6.4 Boltzmann Machines (pg. 249) | |
6.5 Hetero-associators (pg. 255) | |
6.6 Conclusion (pg. 262) | |
6.7 Exercises (pg. 263) | |
Chapter 7. Optimization Methods (pg. 267) | |
7.1 Optimization using Hopfield Networks (pg. 269) | |
7.2 Iterated Gradient Descent (pg. 279) | |
7.3 Simulated Annealing (pg. 280) | |
7.4 Random Search (pg. 285) | |
7.5 Evolutionary Computation (pg. 287) | |
7.6 Conclusion (pg. 300) | |
7.7 Exercises (pg. 302) | |
A. Appendix (pg. 307) | |
A.1 Calculus (pg. 307) | |
A.2 Linear Algebra (pg. 309) | |
A.3 Statistics (pg. 310) | |
B. Appendix (pg. 315) | |
B.1 Iris Data (pg. 315) | |
B.2 Classification of Myoelectric Signals (pg. 316) | |
B.3 Gold Prices (pg. 318) | |
B.4 Clustering Animal Features (pg. 319) | |
B.5 3-D Corners, Grid and Approximation (pg. 319) | |
B.6 Eleven-City Traveling Salesperson Problem (Distances) (pg. 323) | |
B.7 Daily Stock Prices of Three Companies, over the Same Period (pg. 324) | |
B.8 Spiral Data (pg. 327) | |
Bibliography (pg. 331) | |
Index (pg. 339) | |
Contents (pg. vii) | |
Preface (pg. xiii) | |
Chapter 1. Introduction (pg. 1) | |
Chapter 2. Supervised Learning: Single-Layer Networks (pg. 43) | |
Chapter 3. Supervised Learning: Multilayer Networks I (pg. 65) | |
Chapter 4. Supervised Learning: Multilayer Networks II (pg. 111) | |
Chapter 5. Unsupervised Learning (pg. 157) | |
Chapter 6. Associative Models (pg. 217) | |
Chapter 7. Optimization Methods (pg. 267) | |
A. Appendix (pg. 307) | |
B. Appendix (pg. 315) | |
Bibliography (pg. 331) | |
Index (pg. 339) | |
Contents (pg. vii) | |
Preface (pg. xiii) | |
Chapter 1. Introduction (pg. 1) | |
Chapter 2. Supervised Learning: Single-Layer Networks (pg. 43) | |
Chapter 3. Supervised Learning: Multilayer Networks I (pg. 65) | |
Chapter 4. Supervised Learning: Multilayer Networks II (pg. 111) | |
Chapter 5. Unsupervised Learning (pg. 157) | |
Chapter 6. Associative Models (pg. 217) | |
Chapter 7. Optimization Methods (pg. 267) | |
A. Appendix (pg. 307) | |
B. Appendix (pg. 315) | |
Bibliography (pg. 331) | |
Index (pg. 339) | |
Contents (pg. vii) | |
Preface (pg. xiii) | |
Chapter 1. Introduction (pg. 1) | |
Chapter 2. Supervised Learning: Single-Layer Networks (pg. 43) | |
Chapter 3. Supervised Learning: Multilayer Networks I (pg. 65) | |
Chapter 4. Supervised Learning: Multilayer Networks II (pg. 111) | |
Chapter 5. Unsupervised Learning (pg. 157) | |
Chapter 6. Associative Models (pg. 217) | |
Chapter 7. Optimization Methods (pg. 267) | |
A. Appendix (pg. 307) | |
B. Appendix (pg. 315) | |
Bibliography (pg. 331) | |
Index (pg. 339) |
Professor and Chair, Department of Electrical Engineering and Computer Science at Syracuse University.
Professor of Electrical Engineering and Computer Science at Syracuse University.
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