Machine Learning - DISCo

410 SUBJECT INDEX
Normal distribution, 133, 139-140, 143,
151, 165
for noise, 167
in paired tests, 149
Occam's razor, 4, 65-66, 171
Offline learning systems, 385
One-sided bounds, 141, 144
Online learning systems, 385
Optimal brain damage approach, 122
Optimal code, 172
Optimal mistake bounds, 222-223
Optimal policy for selecting actions,
371-372
Optimization problems:
explanation-based learning in, 325
genetic algorithms in, 256, 269
reinforcement learning in, 256
Output encoding in face recognition,
114-1 15
Output units, BACKPROPAGATION weight
update rule for, 102-103
Overfitting, 123
in BACKPROPAGATION algorithm, 108,
11&111
in decision tree learning, 66-69, 76-77,
111
definition of, 67
Minimum Description Length principle
and, 174
in neural network learning, 123
PAC learning, 203-207, 225, 226
of boolean conjunctions, 211-212
definition of, 206-207
training error in, 205
true error in, 204-205
Paired tests, 147-150, 152
Parallelization in genetic algorithms, 268
Partially learned concepts, 38-39
Partially observable states in reinforcement
learning, 369-370
Perceptron training rule, 88-89, 94,95
Perceptrons, 86, 95, 96, 123
representation of boolean functions,
VC dimension of, 219
weight update rule, 88-89, 94, 95
Perfect domain theory, 312-3 13
Performance measure, 6
Performance system, 11-12, 13
Philosophy, influence on machine
learning, 4
Planning problems:
PRODIGY in, 327
case-based reasoning in, 240-241
Policy for selecting actions, 370-372
Population evolution, in genetic algorithms,
260-262
Positive literal, 284, 285
Post-pruning:
in decision tree learning, 68-69, 77,
281
in FOIL algorithm, 291
in LEARN-ONE-RULE, 281
Posterior probability, 155-156, 162
Power law of practice, 4
Power set, 40-42
Predicates, 284, 285
Preference bias, 64, 76, 77
Prior knowledge, 155-156, 336. See also
Domain theory
to augment search operators, 357-361
in Bayesian learning, 155
derivatives of target function, 346-356,
362
in explanation-based learning,
308-309
explicit, use in learning, 329
in human learning, 330
initialize-the-hypothesis approach,
339-346, 362
in PROLOG-EBG, 313
search alteration in inductive-analytical
learning, 339-340, 362
weighting in inductive-analytical
learning, 338, 362
Prioritized sweeping, 380
Probabilistic reasoning, 163
Probabilities:
estimation of, 179-1 80
formulas, 159
maximum likelihood (ML) hypothesis
for prediction of, 167-170
87-88 probability density, 165