Home > hit > pattern_rec > hit_prec_SVC.m

hit_prec_SVC

PURPOSE ^

HIT_PREC_SVC Compute mode regions through Support Vector Classification (SVC)

SYNOPSIS ^

function [sep_hyp,spec]=hit_prec_SVC(X_or,F,s)

DESCRIPTION ^

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.

CROSS-REFERENCE INFORMATION ^

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