Neural Network Design, 2/Ed > 인공지능

1 Introduction
2 Neuron Model and Network Architectures
3 An Illustrative Example
4 Perceptron Learning Rule
5 Signal and Weight Vector Spaces
6 Linear Transformations for Neural Networks
7 Supervised Hebbian Learning
8 Performance Surfaces and Optimum Points
9 Performance Optimization
10 Widrow-Hoff Learning
11 Backpropagation
12 Variations on Backpropagation
13 Generalization
14 Dynamic Networks
15 Associative Learning
16 Competitive Networks
17 Radial Basis Networks
18 Grossberg Network
19 Adaptive Resonance Theory
20 Stability
21 Hopfield Network
22 Practical Training Issues
23 Case Study 1:Function Approximation
24 Case Study 2:Probability Estimation
25 Case Study 3:Pattern Recognition
26 Case Study 4: Clustering
27 Case Study 5: Prediction

Appendices
A Bibliography
B Notation
C Software
I Index