kernelskf.m

this a SVM toolbox,it is very useful for someone who just learn SVM.In order to be undestood easily,

% KERNELSKF kernel Schlesinfer-Kozinec's algorithm. 
% [Alpha,bias,sol]=kernelskf(data,labels,stop,ker,arg,tmax,C) 
% [Alpha,bias,sol,t,kercnt,margin,trnerr]=kernelskf(...) 
% 
% KERNELSKF kernel Schlesinger-Kozinec's algorithm solves the 
%  Support vector Machines problem with quadratic cost function 
%  for classification violations.
%
% Inputs:
%   data [dim x N] training patterns
%   labels [1 x N] labels of training patterns
%   stop [1 x 2] if stop(1) == 1 then stopping condition m*-m < stop(2) 
%     is used else stopping condition  (m*-m)/m < stop(2) is used. 
%     Where m* is the optimial margin and m is the margin of found
%     hyperplane (in the given feature space).
%   ker [string] kernel, see 'help kernel'.
%   arg [...] argument of given kernel, see 'help kernel'.
%   tmax [int] maximal number of iterations.
%   C [real] trade-off between margin and training error.
%  
% Outputs:
%   Alpha [1xN] Lagrangians defining found decision rule.
%   bias [real] bias (threshold) of found decision rule.
%   sol [int] 1 solution is found
%             0 algorithm stoped (t == tmax) before converged.
%            -1 hyperplane with margin greater then epsilon 
%               does not exist.
%   t [int] number of iterations.
%   kercnt [int] number of kernel evaluations.
%   margin [real] margin between classes.
%   trnerr [real] training error.
%
% See also SVM.
%
% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Written Vojtech Franc (diploma thesis) 02.11.1999, 13.4.2000
% Modifications
%  19-Nov-2001, V.Franc
%