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psvc

PURPOSE ^

PSVC Proximal Support Vector Classification

SYNOPSIS ^

function [w,gamma, trainCorr, testCorr, cpu_time, nu]=psvc(A,d,k,nu,output,bal);

DESCRIPTION ^

  PSVC Proximal Support Vector Classification
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 Usage:    [w,gamma,trainCorr, testCorr,cpu_time,nu]=psvc(A,d,k,nu,output,bal)

 A and d are both required, everything else has a default
 An example: [w gamma train test time nu] = psvm(A,d,10);

 Input:
 A is a matrix containing m data in n dimensions each.
 d is a m dimensional vector of 1's or -1's containing
 the corresponding labels for each example in A.
 k is k-fold for correctness purpose
 nu - the weighting factor.
                       -1 - easy estimation
                       0  - hard estimation
                       any other value - used as nu by the algorithm
                       default - 0
 output - indicates whether you want output

 If the input parameter bal is 1
 the algorithm weighs the classes depending on the
 number of points in each class and balance them. 
 It is useful when  the number of point in each class
 is very unbalanced.

 Output:
 w,gamma are the values defining the separating
 Hyperplane w'x-gamma=0 such that:

 w'x-gamma>0 => x belongs to A+
 w'x-gamma<0  => x belongs to A-
 w'x-gamma=0 => x can belongs to both classes
 nu - the estimated or specified value of nu

 For details refer to the paper:
 "Proximal Support Vector Machine Classifiers"
 available at: www.cs.wisc.edu/~gfung
 For questions or suggestions, please email:
 Glenn Fung, gfung@cs.wisc.edu
 Sept 2001.
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CROSS-REFERENCE INFORMATION ^

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