INDEX 523
unlabeled data, 337–341
clustering for classification, 337
co-training, 339–340
EM and co-training, 340–341
unmasking, 358
unsupervised attribute filters in Weka, 395–400
unsupervised discretization, 297–298
unsupervised instance filters in Weka, 400–401
unsupervised learning, 84
UpdateableClassifier, 456, 482
updateClassifier(), 482
User Classifier, 63–65, 388–391
UserClassifier, 388
user interfaces, 367–368
Use training set, 377
utility, category, 260–262V
validation data, 146
variance, 154, 317
Venn diagram, 81
very large datasets, 346–349
“Very simple classification rules perform well
on most commonly used datasets” (Holte),
88
VFI, 414
visualization components in Weka, 430–431
Visualize classifier errors, 387
Visualizepanel, 393
Visualize threshold curve, 378
Vo t e, 417
voted perceptron, 223
Vo t e d Pe r c e p t r o n, 410
voting, 315, 321, 347
voting feature intervals, 136W
weak learners, 325
weather problem example, 10–12
association rules for, 115–117
attribute space for, 292–293
as a classification problem, 42
as a clustering problem, 43–44
converting data to ARFF format, 370cost matrix for, 457
evaluating attributes in, 85–86
infinite rules for, 30
item sets, 113–115
as a numeric prediction problem, 43–44
web mining, 355–356
weight decay, 233
weighted instances, 252
WeightedInstancesHandler, 482
weighting attributes, 237–238
weighting models, 316
weka.associations, 455
weka.attributeSelection, 455
weka.classifiers, 453
weka.classifiers.bayes.NaiveBayesSimple, 472
weka.classifiers.Classifier, 453
weka.classifiers.lazy.IB1, 472
weka.classifiers.lazy.IBk, 482, 483
weka.classifiers.rules.Prism, 472
weka.classifiers.trees, 453
weka.classifiers.trees.Id3, 471, 472
weka.clusterers, 455
weka.core, 451, 452, 482–483
weka.estimators, 455
weka.filters, 455
Weka workbench, 365–483
class hierarchy, 471–483
classifiers, 366, 471–483
command-line interface, 449–459.See also
command-line interface
elementary learning schemes, 472
embedded machine learning, 461–469
example application (classify text files into
two categories), 461–469
Experimenter, 437–447
Explorer, 369–425.See alsoExplorer
implementing classifiers, 471–483
introduction, 365–368
Knowledge Flow interface, 427–435
neural-network GUI, 411
object editor, 366
online documentation, 368
user interfaces, 367–368
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