Data Mining: Practical Machine Learning Tools and ... - LIDeCC

INDEX 517multiclass alternating decision trees, 329, 330,343MultiClassClassifier, 418multiclass learning problems, 334MultilayerPerceptron, 411–413multilayer perceptrons, 223–226, 231, 233multinomial distribution, 95multinomial Naïve Bayes, 95, 96multiple linear regression, 326multiresponse linear regression, 121, 124multistage decision property, 102multivariate decision trees, 199MultiScheme, 417myope, 13NNaiveBayes, 403, 405Naïve Bayes, 91, 278clustering for classification, 337–338co-training, 340document classification, 94–96limitations, 96–97locally weighted, 252–253multinomial, 95, 96power, 96scheme-specific attribute selection, 295–296selective, 296TAN (Tree Augmented Naïve Bayes), 279what can go wrong, 91NaiveBayesMultinominal, 405NaiveBayesSimple, 403NaiveBayesUpdateable, 405NBTree, 408nearest-neighbor learning, 78–79, 128–136,235, 242nested exceptions, 213nested generalized exemplars, 239network scoring, 277network security, 357neural networks, 39, 233, 235, 253neural networks in Weka, 411–413n-gram profiles, 353, 361Nnge, 409noisedata cleansing, 312exemplars, 236–237hand-labeled data, 338robustness of learning algorithm, 306noisy exemplars, 236–237nominal attributes, 49, 50, 56–57, 119Cobweb, 271convert to numeric attributes, 304–305decision tree, 62mixture model, 267model tree, 246subset, 88nominal quantities, 50NominalToBinary, 398–399, 403non-axis-parallel class boundaries, 242Non-Bayesians, 141nonlinear class boundaries, 217–219NonSparseToSparse, 401normal-distribution assumption, 92normalization, 56Normalize, 398, 400normalize(), 480normalized expected cost, 175nuclear family, 47null hypothesis, 155numeric attribute, 49, 50, 56–57axis-parallel class boundaries, 242classification rules, 202Classit, 271converting discrete attributes to, 304–305decision tree, 62, 189–191discretizing, 296–305. See also Discretizingnumeric attributesinstance-based learning, 128, 129interval, 88linear models, 119linear ordering, 349mixture model, 2681R, 86statistical modeling, 92numeric-attribute problem, 11numeric prediction, 43–45, 243–254evaluation, 176–179forward stagewise additive modeling, 325linear regression, 119–120locally weighted linear regression, 251–253