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Contents X~II

4.7 Statistical Classifiers in Data Mining ........................................ 138
140
Bibliography ..........................................................................................
142
Exercises ..............................................................................................

5 Neural Networks ............................................................................... 147

LMS Adjusted Discriminants .................................................... 147
Activation Functions ................................................................. 155
The Perceptron Concept .......................................................... 159
Neural Network Types .............................................................. 167
Multi-Layer Perceptrons ........................................................... 171
5.5.1 The Back-Propagation Algorithm ................................ 172
5.5.2 Practical aspects ......................................................... 175
5.5.3 Time Series ............................................................... 181
Performance of Neural Networks ............................................. 184
5.6.1 Error Measures ............................................................ 184
5.6.2 The Hessian Matrix ..................................................... 186
5.6.3 Bias and Variance in NN Design ................................. 189
5.6.4 Network Complexity .................................................... 192
5.6.5 Risk Minimization ........................................................ 199
Approximation Methods in NN Training ................................... 201
5.7.1 The Conjugate-Gradient Method ................................. 202
5.7.2 The Levenberg-Marquardt Method ............................. 205
Genetic Algorithms in NN Training ........................................... 207
Radial Basis Functions ............................................................. 212
Support Vector Machines ......................................................... 215
Kohonen Networks ................................................................... 223
Hopfield Networks .............................................................. 226
Modular Neural Networks .........................................................
231
235
Neural Networks in Data Mining ...............................................
Bibliography .......................................................................................... 237
Exercises .............................................................................................. 239

6 Structural Pattern Recognition .......................................................
243
6.1 Pattern Primitives ..................................................................... 243
6.1 . 1 Signal Primitives .......................................................... 243
.
245
6.1 2 Image Primitives ..........................................................
6.2 Structural Representations ....................................................... 247
6.2.1 Strings ......................................................................... 247
6.2.2 Graphs ......................................................................... 248
249
6.2.3 Trees ...........................................................................
6.3 Syntactic Analysis ................................................................... 250