INDEX 517
multiclass alternating decision trees, 329, 330,
343
MultiClassClassifier, 418
multiclass learning problems, 334
MultilayerPerceptron, 411–413
multilayer perceptrons, 223–226, 231, 233
multinomial distribution, 95
multinomial Naïve Bayes, 95, 96
multiple linear regression, 326
multiresponse linear regression, 121, 124
multistage decision property, 102
multivariate decision trees, 199
MultiScheme, 417
myope, 13N
NaiveBayes, 403, 405
Naïve Bayes, 91, 278
clustering for classification, 337–338
co-training, 340
document classification, 94–96
limitations, 96–97
locally weighted, 252–253
multinomial, 95, 96
power, 96
scheme-specific attribute selection, 295–296
selective, 296
TAN (Tree Augmented Naïve Bayes), 279
what can go wrong, 91
NaiveBayesMultinominal, 405
NaiveBayesSimple, 403
NaiveBayesUpdateable, 405
NBTree, 408
nearest-neighbor learning, 78–79, 128–136,
235, 242
nested exceptions, 213
nested generalized exemplars, 239
network scoring, 277
network security, 357
neural networks, 39, 233, 235, 253
neural networks in Weka, 411–413
n-gram profiles, 353, 361
Nnge, 409
noise
data cleansing, 312exemplars, 236–237
hand-labeled data, 338
robustness of learning algorithm, 306
noisy exemplars, 236–237
nominal attributes, 49, 50, 56–57, 119
Cobweb, 271
convert to numeric attributes, 304–305
decision tree, 62
mixture model, 267
model tree, 246
subset, 88
nominal quantities, 50
NominalToBinary, 398–399, 403
non-axis-parallel class boundaries, 242
Non-Bayesians, 141
nonlinear class boundaries, 217–219
NonSparseToSparse, 401
normal-distribution assumption, 92
normalization, 56
Normalize, 398, 400
normalize(), 480
normalized expected cost, 175
nuclear family, 47
null hypothesis, 155
numeric attribute, 49, 50, 56–57
axis-parallel class boundaries, 242
classification rules, 202
Classit, 271
converting discrete attributes to, 304–305
decision tree, 62, 189–191
discretizing, 296–305.See alsoDiscretizing
numeric attributes
instance-based learning, 128, 129
interval, 88
linear models, 119
linear ordering, 349
mixture model, 268
1R, 86
statistical modeling, 92
numeric-attribute problem, 11
numeric prediction, 43–45, 243–254
evaluation, 176–179
forward stagewise additive modeling, 325
linear regression, 119–120
locally weighted linear regression, 251–253P088407-INDEX.qxd 4/30/05 11:25 AM Page 517