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Data Mining, 4th Edition by Christopher J. Pal, Mark A. Hall, Eibe Frank, Ian H. Witten

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in finding nearest-neighbor, 137–138, 137f
for training instances, 137f
updating, 138–139
Keras, 465–466
Kernel density estimation, 361–362
Kernel logistic regression, 261
Kernel perceptron, 260–261
Kernel regression, 403
Kernel ridge regression, 258–259
computational expense, 259
computational simplicity, 259
drawback, 259
Kernels, conditional probability models using, 402–403
Kernel trick, 258
K-means algorithm, 355
K-means clustering, 142–143
iterations, 144
k-means++, 144
seeds, 144
K-nearest-neighbor method, 85
Knowledge, 37
background, 508
metadata, 513
prior domain, 513
Knowledge Flow interface, 554, 555, 564–567See also Weka workbench
Associations panel, 566
Classifiers folder, 566
Clusters folder, 566
components, 566 ...

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