最小二乘支持向量机LS-SVMlab工具箱
最小二乘支持向量机LS-SVMlab工具箱：http://www.esat.kuleuven.be/sista/lssvmlab/% >> = trainlssvm({X,Y,type,gam,sig2})% >> = trainlssvm({X,Y,type,gam,sig2,kernel})
% >> = trainlssvm({X,Y,type,gam,sig2,kernel,preprocess})
%
% Outputs
% alpha : N x m matrix with support values of the LS-SVM
% b : 1 x m vector with bias term(s) of the LS-SVM
% Inputs
% X : N x d matrix with the inputs of the training data
% Y : N x 1 vector with the outputs of the training data
% type : 'function estimation' ('f') or 'classifier' ('c')
% gam : Regularization parameter正则化参数
% sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel')基宽因子
% kernel(*) : Kernel type (by default 'RBF_kernel')
% preprocess(*) : 'preprocess'(*) or 'original'
支持向量机SVM工具箱：http://www.csie.ntu.edu.tw/~cjlin/libsvm/
% Usage: model = svmtrain(training_label_vector, training_instance_matrix, 'libsvm_options');
% libsvm_options:
% -s svm_type : set type of SVM (default 0)
% 0 -- C-SVC (multi-class classification)
% 1 -- nu-SVC (multi-class classification)
% 2 -- one-class SVM
% 3 -- epsilon-SVR (regression)
% 4 -- nu-SVR (regression)
% -t kernel_type : set type of kernel function (default 2)
% 0 -- linear: u'*v
% 1 -- polynomial: (gamma*u'*v + coef0)^degree
% 2 -- radial basis function: exp(-gamma*|u-v|^2)
% 3 -- sigmoid: tanh(gamma*u'*v + coef0)
% 4 -- precomputed kernel (kernel values in training_instance_matrix)
% -d degree : set degree in kernel function (default 3)
% -g gamma : set gamma in kernel function (default 1/num_features)
% -r coef0 : set coef0 in kernel function (default 0)
% -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
% -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
% -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
% -m cachesize : set cache memory size in MB (default 100)
% -e epsilon : set tolerance of termination criterion (default 0.001)
% -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
% -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
% -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
% -v n : n-fold cross validation mode
% -q : quiet mode (no outputs)
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可参考文章：http://www.doc88.com/p-959212556942.html
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