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HH8t$HtLƺIHL ZAuS3H\$0Ht$8H|$HH A^ù\ H\$WH @5S~Sȉ)S؈D$8=XuD  %X&3@t3H\$0H _ùHHX L@PHHVWAVH@ILu9R3BwEH#Hu D$0G؉D$0LƋI؉D$0LƋI؉D$0u8u4L3IL3I\Hy#HtL3Itu@LƋI,؉D$0t)H?#Hu X\$0LƋI؉D$03ۉ\$0H\$xH@A^_^H\$Ht$WH IHu;LNjHH\$0Ht$8H _@SH H3HHȺ H [H%HL$H8 t)H QHD$8HRHD$8HHRHkRHPHD$@HQP PPHkH PHHkH NHL HkH uNHL H !H8@SVWH@H H3E3HT$`Ha Ht9Hd$8HL$hHT$`LHL$0LHL$pHL$(3H\$  ǃ|H@_^[@SH HHH U!WH HSHH3 Hh!HHH [HaH`!HAHE!HH@SH HHH WH HSHHH4!HHH [HaH,!HAH!HH@SH HHH WH HSHH{HH [Hu HHcH\$WH HW HHH@t HH\$0HH _HHHL$ H7HL$ HHHL$ &H78HL$ HyHHEAH\$ UHH H%h%J%<%%%%%%%%%%%%%%%%3ffH@YHPtX@UH H0HMXH ]H[HhDYHpZHYHZHh4XHhW@UH H0HxH ]@UH HM@H ]@UH HM8H ]z@UH0HHHL$(T$ L LEpUhHM`7H0]@UHH3Ɂ8]r\B&VJh~PH$,6x :\~tbX@$r`VL>0Usage: 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) Cross Validation Mean squared error = %g Cross Validation Squared correlation coefficient = %g Cross Validation Accuracy = %g%% n-fold cross validation: n must >= 2 Unknown option -%c Length of label vector does not match # of instances. Wrong input format: sample_serial_number out of range transposeError: cannot transpose training instance matrix Error: label vector and instance matrix must be double Error: label vector should not be in sparse format fullError: cannot generate a full training instance matrix Error: %s Error: can't convert libsvm model to matrix structure: %s ???Y@Y@MbP?c_svcnu_svcone_classepsilon_svrnu_svrlinearpolynomialrbfsigmoidprecomputed" "( " "( "P " "8H`"8`"`"th"t`   (  ЊP()Z?A WARNING: using -h 0 may be faster .* WARNING: reaching max number of iterations optimization finished, #iter = %d x)p^@DpG@J )F0J(0HPJ `)nu = %f C = %f epsilon = %f obj = %f, rho = %f nSV = %d, nBSV = %d Line search fails in two-class probability estimates Reaching maximal iterations in two-class probability estimates Exceeds max_iter in multiclass_prob Prob. model for test data: target value = predicted value + z, z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g WARNING: training data in only one class. See README for details. WARNING: class label %d specified in weight is not found Total nSV = %d WARNING: # folds > # data. 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