INDEX 507
automatic filtering, 315
averaging over subnetworks, 283
axis-parallel class boundaries, 242B
background knowledge, 348
backpropagation, 227–233
backtracking, 209
backward elimination, 292, 294
backward pruning, 34, 192
bagging, 316–319
Bagging, 414–415
bagging with costs, 319–320
bag of words, 95
balanced Winnow, 128
ball tree, 133–135
basic methods.Seealgorithms-basic methods
batch learning, 232
Bayes, Thomas, 141
Bayesian classifier.SeeNaïve Bayes
Bayesian clustering, 268–270
Bayesian multinet, 279–280
Bayesian network, 141, 271–283
AD tree, 280–283
Bayesian multinet, 279–280
caveats, 276, 277
counting, 280
K2, 278
learning, 276–283
making predictions, 272–276
Markov blanket, 278–279
multiplication, 275
Naïve Bayes classifier, 278
network scoring, 277
simplifying assumptions, 272
structure learning by conditional
independence tests, 280
TAN (Tree Augmented Naïve Bayes), 279
Weka, 403–406
Bayesian network learning algorithms, 277–283
Bayesian option trees, 328–331, 343
Bayesians, 141
Bayesian scoring metrics, 277–280, 283
Bayes information, 271
BayesNet, 405Bayes’s rule, 90, 181
beam search, 34, 293
beam width, 34
beer purchases, 27
Ben Ish Chai, 358
Bernoulli process, 147
BestFirst, 423
best-first search, 293
best-matching node, 257
bias, 32
defined, 318
language, 32–33
multilayer perceptrons, 225, 226
overfitting-avoidance, 34–35
perceptron learning rule, 124
search, 33–34
what is it, 317
bias-variance decomposition, 317, 318
big data (massive datasets), 346–349
binning
equal-frequency, 298
equal-interval, 298
equal-width, 342
binomial coefficient, 218
bits, 102
boolean, 51, 68
boosting, 321–325, 347
boosting in Weka, 416
bootstrap aggregating, 318
bootstrap estimation, 152–153
British Petroleum, 28
buildClassifier(), 453, 472, 482C
C4.5, 105, 198–199
C5.0, 199
calm computing, 359, 362
capitalization conventions, 310
CAPPS (Computer Assisted Passenger Pre-
Screening System), 357
CART (Classification And Regression Tree), 29,
38, 199, 253
categorical attributes, 49.See alsonominal
attributes
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