www.pudn.com > osu_svm3.00.zip > RbfSVC.m

function [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = RbfSVC(Samples, Labels, Gamma, C) 
% USAGES:  
%    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = RbfSVC(Samples, Labels) 
%    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = RbfSVC(Samples, Labels, Gamma) 
%    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = RbfSVC(Samples, Labels, Gamma, C) 
% 
% DESCRIPTION:  
%   Construct a non-linear SVM classifier with a radial based kernel, or Guassian kernel,  
%     from the training Samples and Labels 
% 
% INPUTS: 
%   Samples: all the training patterns. (a row of column vectors) 
%   Lables: the corresponding class labels for the training patterns in Samples, (a row vector) 
%   Gamma: parameters of the radial based kernel, which has the form 
%            of (exp(-Gamma*|X(:,i)-X(:,j)|^2)). (default 1) 
%   C: Cost of the constrain violation  (default 1) 
% 
% OUTPUTS: 
%    AlphaY    - Alpha * Y, where Alpha is the non-zero Lagrange Coefficients, and 
%                    Y is the corresponding Labels, (L-1) x sum(nSV); 
%                All the AlphaYs are organized as follows: (pretty fuzzy !) 
%      				classifier between class i and j: coefficients with 
%			  	         i are in AlphaY(j-1, start_Pos_of_i:(start_Pos_of_i+1)-1), 
%				         j are in AlphaY(i, start_Pos_of_j:(start_Pos_of_j+1)-1) 
%    SVs       - Support Vectors. (Sample corresponding the non-zero Alpha), M x sum(nSV), 
%                All the SVs are stored in the format as follows: 
%                 [SVs from Class 1, SVs from Class 2, ... SVs from Class L]; 
%    Bias      - Bias of all the 2-class classifier(s), 1 x L*(L-1)/2; 
%    Parameters -  Output parameters used in training; 
%    nSV       -  numbers of SVs in each class, 1xL; 
%    nLabel    -  Labels of each class, 1xL. 
% 
% By Junshui Ma, and Yi Zhao (02/15/2002) 
% 
 
if (nargin < 2) & (nargin > 4) 
   disp(' Incorrect number of input variables.\n'); 
   help RbfSVC; 
   return; 
else 
   if (nargin == 2) 
       Parameters = [2]; 
       [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters); 
   elseif  (nargin == 3) 
       Parameters = [2 1 Gamma]; 
       [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters); 
   elseif  (nargin == 4) 
       Parameters = [2 1 Gamma 1 C]; 
       [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters); 
   end 
end