文件列表:

libsvm_chi_ksirg\a1a.test (2215057, 2009-09-20)
libsvm_chi_ksirg\a1a.train (114818, 2009-09-20)
libsvm_chi_ksirg\a9a (2329875, 2013-01-21)
libsvm_chi_ksirg\a9a.t (1164628, 2013-01-21)
libsvm_chi_ksirg\chi_square_kernel.m (995, 2012-11-23)
libsvm_chi_ksirg\Data\a1a.test (2215057, 2009-09-20)
libsvm_chi_ksirg\Data\a1a.train (114818, 2009-09-20)
libsvm_chi_ksirg\Data\a9a (2329875, 2013-01-21)
libsvm_chi_ksirg\Data\a9a.t (1164628, 2013-01-21)
libsvm_chi_ksirg\dataset_stat.m (1311, 2013-01-08)
libsvm_chi_ksirg\exp_chi_square_kernel.m (641, 2012-11-29)
libsvm_chi_ksirg\heart_scale (27670, 2012-05-19)
libsvm_chi_ksirg\LibSVM_parallel.asv (999, 2013-01-30)
libsvm_chi_ksirg\LibSVM_parallel.m (999, 2013-01-30)
libsvm_chi_ksirg\Makefile (559, 2012-05-19)
libsvm_chi_ksirg\Makefile.win (1087, 2012-05-19)
libsvm_chi_ksirg\matlab\libsvmread.c (4014, 2012-05-19)
libsvm_chi_ksirg\matlab\libsvmread.mexw32 (8192, 2014-09-22)
libsvm_chi_ksirg\matlab\libsvmread.mexw64 (10752, 2013-01-29)
libsvm_chi_ksirg\matlab\libsvmwrite.c (2148, 2012-05-19)
libsvm_chi_ksirg\matlab\libsvmwrite.mexw32 (6656, 2014-09-22)
libsvm_chi_ksirg\matlab\libsvmwrite.mexw64 (9216, 2013-01-29)
libsvm_chi_ksirg\matlab\make.m (1217, 2013-01-29)
libsvm_chi_ksirg\matlab\Makefile (1499, 2012-05-19)
libsvm_chi_ksirg\matlab\svmpredict.c (9263, 2012-05-19)
libsvm_chi_ksirg\matlab\svmpredict.mexw64 (26112, 2013-01-29)
libsvm_chi_ksirg\matlab\svmtrain.c (11685, 2013-01-29)
libsvm_chi_ksirg\matlab\svmtrain.mexw64 (66048, 2013-01-29)
libsvm_chi_ksirg\matlab\svm_model_matlab.c (7722, 2012-05-19)
libsvm_chi_ksirg\matlab\svm_model_matlab.h (201, 2012-05-19)
libsvm_chi_ksirg\svm-predict.c (5381, 2012-05-19)
libsvm_chi_ksirg\svm-scale.c (7042, 2012-05-19)
libsvm_chi_ksirg\svm-train.c (9242, 2013-01-29)
libsvm_chi_ksirg\svm-train.c.bak (9018, 2012-11-27)
libsvm_chi_ksirg\svm.cpp (65407, 2013-01-29)
libsvm_chi_ksirg\svm.cpp.bak (65056, 2012-11-29)
libsvm_chi_ksirg\svm.def (434, 2012-05-19)
libsvm_chi_ksirg\svm.h (3227, 2013-01-29)
... ...

Modified LibSVM with CHI^2 Kernel and exponential CHI^2, modifications done by Krzysztof Sopya (http://wmii.uwm.edu.pl/~ksopyla/libsvm_chi2) This library contains two definition of Chi Squared kernels, which are the moste popular in publication 1) K(x,y)= 1-2*sum( (xi-yi)^2/(xi+yi)); # normal CHI^2 2) K(x,y)= sum( (xi*yi)/(xi+yi)); # normalized CHI^2 3) K(x,y)= exp(-gamma*Chi-Squared(x,y)); These kernels are avaliable through matlab interface. Build ====================== There are already compiled .mex files for windows x*** architecture, but if you want build for different platform just write matlab>make Usage ====================== Two new parameter options was added, now parameter "t" can accept values equals 5,6,7 matlab> model = svmtrain(training_label_vector, training_instance_matrix, 'libsvm_options'); options: -s svm_type : set type of SVM (default 0)" 0 -- C-SVC" 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR -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_set_file) 5 -- chi-squaree kernel k(x,y)=1-2*sum( (xi-yi)^2/(xi+yi)) 6 -- norm chi-squaree kernel k(x,y)=sum( xi*yi/(xi+yi 7 -- exponential chi-squaree kernel k(x,y)=exp(-gamma*sum( (xi-yi)^2/(xi+yi))) -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) Example ====================== See test_libsvm.m Classification with chi^2 kernel, with C=4 tr_path='a1a.train'; tst_path='a1a.train'; [trYY trXX]=libsvmread(tr_path); [tstYY tstXX]=libsvmread(tst_path); trXXn=trXX; %l1 - norm trXXn=bsxfun(@rdivide,trXXn,sum(trXXn,2)); tic; model = svmtrain(trYY, trXXn,'-c 4 -t 5'); modelTime=toc; tstXXn=tstXX; tstXXn=bsxfun(@rdivide,tstXXn,sum(tstXXn,2)); %l1 - norm tic [pred, acc, dec_vals] = svmpredict(tstYY, tstXXn, model); predTime = toc; ss=sprintf('libsvm chi^2 acc=%0.5g modeltime=%g predtime=%g \n',acc(1),modelTime, predTime); disp(ss); Additional Information ====================== LibSVM matlab interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng, Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer Science, National Taiwan University. The current version was prepared by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please cite LIBSVM as follows Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm For any question, please contact Chih-Jen Lin , or check the FAQ page: http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q9:_MATLAB_interface

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：