TEST_SC: apply statistical and SVM classifier to test data R = test_sc(CC,D,TYPE [,target_Classlabel]) R.output output: "signed" distance for each class. This represents the distances between sample D and the separating hyperplane The "signed distance" is possitive if it matches the target class, and and negative if it lays on the opposite side of the separating hyperplane. R.classlabel class for output data The target class is optional. If it is provided, the following values are returned. R.kappa Cohen's kappa coefficient R.ACC Classification accuracy R.H Confusion matrix The classifier CC is typically obtained by TRAIN_SC. If a statistical classifier is used, TYPE can be used to modify the classifier. TYPE = 'MDA' mahalanobis distance based classifier TYPE = 'MD2' mahalanobis distance based classifier TYPE = 'MD3' mahalanobis distance based classifier TYPE = 'GRB' Gaussian radial basis function TYPE = 'QDA' quadratic discriminant analysis TYPE = 'LD2' linear discriminant analysis TYPE = 'LD3', 'LDA', 'FDA, 'FLDA' (Fisher's) linear discriminant analysis TYPE = 'LD4' linear discriminant analysis TYPE = 'GDBC' general distance based classifier see also: TRAIN_SC References: [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. John Wiley & Sons, 2001.
Package: nan