| ### 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

### T

Tanh function, 1, 315-316
back-propagation and, 54
error surface and, 115, 118, 119, 123, 128
generalization and, 251
for single-layer networks, 15
Target, effective, 278-281, 289
Target outputs, introduction to, 7-10
Teacher, 7, 11, 12-14, 49
Tessellation, Voronoi, 215-216
Threshold term, 17-18. See also Linear threshold units
Tiling algorithm, 206-209
Trace(H), 81-82
Training. See also Algorithms; Supervised learning
definition of, 7
jitter and, 277-292
optimal amount of, 250
Training (cont.)
as optimization problem, 155
over-, 198, 237, 241-242, 249-251, 292
steps in, 49
Training set size. See Size
Training time, 67-85
factors that increase, 68-69
initialization and, 97, 105, 106
momentum and, 74-77, 85-95
pruning and, 219, 237
scaling of, 68-70
training set size and, 68, 71, 199
variations to improve, 135-153
Two-hidden-layers networks, 32-33, 38-39, 109
Two-spirals problem, 108, 144, 254

Books24x7.com, Inc. © 1999-2001 – Feedback