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
E
Early stopping,
265
-
266
Effective learning rate,
77
,
87
,
88
,
90
Effective target,
278
-
281
,
289
Eigenvalues,
127
-
128
.
See also
Hessian matrix
initialization and,
106
learning rate and,
81
-
84
,
85
node splitting and,
215
search then converge method and,
147
Energy
pruning and,
227
-
228
simulated annealing and,
177
Entropic error function,
123
.
See also
Cross-entropy error function
Entropy,
271
-
272
Epochs,
58
Error function,
7
,
37
.
See also
E (t) curves
constructive methods and,
198
-
201
,
205
-
206
cross-entropy,
9
,
50
,
110
,
155
,
276
entropic,
123
initialization and,
110
learning rate and,
71
-
80
LMS-threshold,
123
,
124
MSE (
see
Mean squared error function
)
penalty-term methods and,
220
-
221
,
226
-
231
RMS (
see
Root mean square error function
)
selection of,
253
sensitivity methods and,
220
,
221
-
225
SSE (
see
Sum of squared errors function
)
system energy and,
177
in training steps,
49
-
50
,
52
-
69
Error surface
algorithm assumptions and,
120
characteristics of,
113
-
117
gain scaling and,
114
,
132
-
134
Hessian matrix and,
127
-
132
learning rate and,
72
local minima of,
115
-
116
,
117
,
121
-
126
momentum and,
87
-
89
quadratic function and,
179
radial features of,
114
-
116
stair-steps on,
113
-
114
,
121
total gradient of,
117
troughs and ridges of,
116
-
117
weight-space symmetries for,
118
-
119
E(t)
curves
constructive methods and,
199
,
201
learning rate and,
77
-
80
momentum and,
90
-
93
Evaluation-only methods
deterministic,
159
-
163
stochastic,
175
-
179
Evolutionary algorithm.
See
Genetic algorithm
Exclusive-OR function,
18
,
19
,
107
,
251
-
253
Exploratory moves,
160
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