| ### Index
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Russell D. Reed and Robert J. Marks II
Copyright © 1999 Massachusetts Institute of Technology

## Index

### C

Capacity, 41-47. See also Representational capabilities
Cascade-correlation algorithm, 201-204, 268
Cauchy's method, 150, 165-166
Cell bodies, 1
CGD (conjugate gradient descent), 166-169, 179, 182, 252
Chain rule, 53, 66-67
Classical optimization methods. See also Genetic algorithm; Optimization methods
best-step steepest descent, 165-166
conjugate gradient descent, 166-169, 179, 182, 252
deterministic, 158, 159-175
Gauss-Newton, 130, 172
gradient-based, 158, 163-175, 182
Hooke-Jeeves pattern search, 160-161
Levenberg-Marquardt, 128, 144, 172-173, 182
Nelder-Mead simplex search, 161
Newton's, 128, 145, 152, 170-172, 179, 294-295
Powell's conjugate direction, 161-163
quasi-Newton, 174-175
simulated annealing, 157, 176-178
Classical optimization methods (cont.)
stochastic, 158, 175-179
Classification
initialization and, 107-109, 110
regression versus, 156-157
Clipped linear function, 317
Cluster centers, 107
Clustering, 158
Competition, and pruning, 232-233
Complexity, 241-242, 244-247
Computation speed, 4-5
Conjugate gradient descent (CGD), 166-169, 179, 182, 252
Conjugate methods, 144, 152, 161-163
Connected, fully, 3
Constraints, 240-241, 244-245
Constructive methods, 197-199, 217
cascade-correlation, 201-204, 268
dynamic node creation, 199-201
generalization and, 268
Marchand's, 209-212
meiosis networks and, 212-213
node splitting, 212-215
pruning and, 198, 201
tiling, 206-209
upstart, 204-206
Voronoi tessellation, 215-216
Convergence time. See Training time
Convex regions, 33
Convolution, 277-281, 311-314
Cost function, 7, 155-156. See also Error function; Objective function
Cross-entropy error function, 9, 50, 110, 155, 276
Crossover, 186, 187, 188
Cross-validation, 257-258, 273, 291

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