Mathematical Perspectives on Neural Networks

By Paul Smolensky; Michael C. Mozer et al. | Go to book overview

Subject Index
Acceptance probability, 168-169, 173-174, 197, 199-200
Activation patterns, 2-3

well fonnedness, 18, 33, 246

Adaline rule, 261-262, 334, 338, 341-342
Additive nets, 273
Algorithmic problem, 63--65
Algorithms, 47
heuristic, 143
local search, 144, 147, 150
genetic, 163-164
performance analysis, 154-162
pruning, 434
simplex, 157
threshold, 144, 164-167
tree sort, 414
variable-depth search, 151-153
AMNIAC, 43, 68, 70
Analog computability theory, 9, 18, 36-37, 44
Analog computer, 38-39, 225-240
black boxes, 228
feedback, 229-230
integrator, 228-229
Analog gating, 51
Analog generable function, 36-38
characterization, 232-233
computability, 236-238
definition, 227-232
turing machines, 233-236
Approximation
annealed, 329
errors, 475, 477
function, 521-524
problem, 475, 486
ARMA, see Autoregressive moving average system
Artificial neural networks
binary perceptron, 637-639
complexity, 761-762
dynamic, 634-639
hidden Markov models, 630-642
location families, 729-731
location-scale families, 731-732
overfitting, 760
parametric models, 724-727
parametric statistical estimation, 719-773
quantile families, 732
supervised networks, 729
training, 639-642, 760-761
unsupervised networks, 733-737
The Art of the Fugue, 398
Assignment problem, 520-521
Associative memory, 259-260, 331-333, 581-583
Attractors, 253-254, 282-284
global, 283
smooth, 254, 291-293
Automata nets, 22, 60-61
Autoregressive models, 400-401
Autoregressive moving average system, 265, 267, 352-353

models, 401-402


B
Backpropagation, 114, 460, 533-565
binomial case, 542-543
dynamic, 364, 375-376, 378-379

-855-

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