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Introduction to Pattern Recognition: A Matlab Approach
book

Introduction to Pattern Recognition: A Matlab Approach

by Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras
March 2010
Intermediate to advanced
150 pages
7h 13m
English
Academic Press
Content preview from Introduction to Pattern Recognition: A Matlab Approach
134 CHAPTER 4 Feature Selection
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FIGURE 4.9
Plot of the patterns of the two classes, employing a 3-feature combination. Observe that this combination
results in well-separated classes in the 3-dimensional feature space.
'Homogeneity 0','Homogeneity 90','Homogeneity 45','Homogeneity 135'};
fNames=featureNames(inds);
fNames=fNames(cLbest);
plotData(c1_test(cLbest,:),c2_test(cLbest,:),1:3,fNames);
Step 8. Classify the feature vectors of the test data using the k-NN classifier (k = 3) and compute
the classification error. For t hi s, use functions k_nn_classifier and compute_error,whichwere
introduced
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Publisher Resources

ISBN: 9780123744869