


HIT_PREC_SVC Compute mode regions through Support Vector Classification (SVC)
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DESCRIPTION
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[sep_hyp,spec]=hit_prec_SVC(X_or,F,s)
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INPUT
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X_or: matrix containing regressors. Each row is a datapoint.
F: structure containing information about the mode datasets (the
classified datapoints that are also inliers, i.e. not discarded during
regression). Type 'help hit_regression' for a description of its fields.
s: number of regions to be found.
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OUTPUT
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sep_hyp{i,j} stores the hyperplane separating the i-th and j-th mode
datasets according to the formula sep_hyp{i,j}*[x 1]=0. By convention,
sep_hyp{i,i}=[0 .. 0] thus defining a fictious hyperplane.
spec: structure containing information about the results
spec.correctness(i,j), i>j: correctness in separating regressors of the
i-th mode from regressors of the j-th mode. It is the ratio between the
number of regressors correctly classified divided the total number of
regressors of the i-th and j-th mode.