SVM_MATLAB代码

%
% SVMTRAIN(...,'QUADPROG_OPTS',OPTIONS) allows you to pass an OPTIONS
% structure created using OPTIMSET to the QUADPROG function when using
% the 'QP' method. See help optimset for more details.
% used by SVMCLASSIFY for classification. GROUP is a column vector of
% values of the same length as TRAINING that defines two groups. Each
% element of GROUP specifies the group the corresponding row of TRAINING
% classes = svmclassify(svmStruct,data(test,:),'showplot',true);
% % See how well the classifier performed
% classperf(cp,classes,test);
% Vandewalle, J., Least Squares Support Vector Machines,
% World Scientific, Singapore, 2002.
% [3] Scholkopf, B., Smola, A.J., Learning with Kernels,
% cp = classperf(groups);
% % Use a linear support vector machine classifier
% svmStruct = svmtrain(data(train,:),groups(train),'showplot',true);
% MIT Press, Cambridge, MA. 2002.
%
% SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional
% arguments to kernel functions.
% set defaults
end
numoptargs = nargin -2;
optargs = varargin;
% grp2idx sorts a numeric grouping var ascending, and a string grouping
% var by order of first occurrence
% belongs to. GROUP can be a numeric vector, a string array, or a cell
% array of strings. SVMTRAIN treats NaNs or empty strings in GROUP as
% missing values and ignores the corresponding rows of TRAINING.
% following strings or a function handle:
%
% 'linear' Linear kernel or dot product
% 'quadratic' Quadratic kernel
% 'polynomial' Polynomial kernel (default order 3)
[g,groupString] = grp2idx(groupnames);
% cp.CorrectRate
%
% See also CLASSIFY, KNNCLASSIFY, QUADPROG, SVMCLASSIFY.
% Copyright 2004 The MathWorks, Inc.
% $Revision: 1.1.12.1 $ $Date: 2004/12/24 20:43:35 $
%
% SVMTRAIN(...,'METHOD',METHOD) allows you to specify the method used
% to find the separating hyperplane. Options are
%
% 'QP' Use quadratic programming (requires the Optimization Toolbox)
%
% SVMTRAIN(...,'KERNEL_FUNCTION',KFUN) allows you to specify the kernel
% function KFUN used to map the training data into kernel space. The
% default kernel function is the dot product. KFUN can be one of the
% groups = ismember(species,'setosa');
% % Randomly select training and test sets
% [train, test] = crossvalind('holdOut',groups);
else
useQuadprog = false;
end
% set default kernel function
kfun = @linear_kernel;
% check inputs
if nargin < 2
error(nargchk(2,Inf,nargin))
%
% Example:
% % Load the data and select features for classification
% load fisheriris
% data = [meas(:,1), meas(:,2)];
% % Extract the Setosa class
%
% SVMStruct = SVMTRAIN(TRAINING,GROUP) trains a support vector machine
% classifier using data TRAINING taken from two groups given by GROUP.
% SVMStruct contains information about the trained classifier that is
%
% SVMTRAIN(...,'SHOWPLOT',true), when used with two-dimensional data,
% creates a plot of the grouped data and plots the separating line for
% the classifier.
% parameters of the Multilayer Perceptron (mlp) kernel. The mlp kernel
% requires two parameters, P1 and P2, where K = tanh(P1*U*V' + P2) and P1
% > 0 and P2 < 0. Default values are P1 = 1 and P2 = -1.
% References:
% [1] Kecman, V, Learning and Soft Computing,
% MIT Press, Cambridge, MA. 2001.
% [2] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B.,
% for example @KFUN, or an anonymous function
%
% A kernel function must be of the form
%
% function K = KFUN(U, V)
%
% The returned value, K, is a matrix of size M-by-N, where U and V have M
% 'rbf' Gaussian Radial Basis Function kernel
% 'mlp' Multilayer Perceptron kernel (default scale 1)
% function A kernel function specified using @,
% and N rows respectively. If KFUN is parameterized, you can use
% anonymous functions to capture the problem-dependent parameters. For
% example, suppose that your kernel function is
% 'LS' Use least-squares method