-------------------------------------------------- Document for MATLAB interface of LIBSVM --------------------------------------------------Introduction============This tool provides a simple interface to LIBSVM, a library for support vectormachines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use asthe usage and the way of specifying parameters is the same as that of LIBSVM.Installation============On Unix systems, we recommend using GNU g++ as your compiler andtype 'make' to build 'svmtrain.mexglx' and 'svmpredict.mexglx'.Note that we assume your MATLAB is installed in '/usr/local/matlab',if not, please change MATLABDIR in Makefile.Example:        linux> makeOn Windows systems, pre-built 'svmtrain.dll' and 'svmpredict.dll' areincluded in this package, so no need to conduct installation. If youhave modified the sources and would like to re-build the package, type'mex -setup' in MATLAB to choose a compiler for mex first. Then type'make' to start the installation.Example:        matlab> mex -setup        (ps: MATLAB will show the following messages to setup default compiler.)        Please choose your compiler for building external interface (MEX) files:         Would you like mex to locate installed compilers [y]/n? y        Select a compiler:         [1] Microsoft Visual C/C++ version 6.0 in C:\Program Files\Microsoft Visual Studio         [0] None         Compiler: 1        Please verify your choices:         Compiler: Microsoft Visual C/C++ 6.0         Location: C:\Program Files\Microsoft Visual Studio         Are these correct?([y]/n): y        matlab> makeUsage=====matlab> model = svmtrain(training_label_vector, training_instance_matrix, [,'libsvm_options']);        -training_label_vector:            An m by 1 vector of training labels.        -training_instance_matrix:            An m by n matrix of m training instances with n features.            It can be dense or sparse.        -libsvm_option:            A string of training options in the same format as that of LIBSVM.matlab> [predicted_label, accuracy] = svmpredict(testing_label_vector, testing_instance_matrix, model [,'libsvm_option']);        -testing_label_vector:            An m by 1 vector of prediction labels. If labels of test            data are unknown, simply use any random values.        -testing_instance_matrix:            An m by n matrix of m testing instances with n features.            It can be dense or sparse.        -model:            The output of svmtrain.        -libsvm_option:            A string of testing options in the same format as that of LIBSVM.Returned Model Structure========================The 'svmtrain' function returns a model which can be used for futureprediction.  It is a structure and is organized as [Parameters, nr_class,totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]:        -Parameters: parameters        -nr_class: number of classes; = 2 for regression/one-class svm        -totalSV: total #SV        -rho: -b of the decision function(s) wx+b        -Label: label of each class; empty for regression/one-class SVM        -ProbA: pairwise probability information; empty if -b 0 or in one-class SVM        -ProbB: pairwise probability information; empty if -b 0 or in one-class SVM        -nSV: number of SVs for each class; empty for regression/one-class SVM        -sv_coef: coefficients for SVs in decision functions        -SVs: support vectorsIf you do not use the option '-b 1', ProbA and ProbB are emptymatrices. If the '-v' option is specified, cross validation isconducted and the returned model is just a scalar: cross-validationaccuracy for classification and mean-squared error for regression.More details about this model can be found in LIBSVM FAQ(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVMimplementation document(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).Result of Prediction====================The function 'svmpredict' has two outputs. The first one,predicted_label, is in general a vector of predicted labels. If '-b 1'is specified as an option of 'svmpredict' and the input modelpossesses probability information, it is a matrix where additionalelements in each row are probabilities that the test data is in eachclass. Note that the order of classes is the same as Label in themodel structure. The second output, accuracy, is a vector includingaccuracy (for classification), mean squared error, and squaredcorrelation coefficient (for regression).Examples========matlab> load heart_scale.matmatlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2');matlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training dataFor probability estimates, you need '-b 1' for training and testing:matlab> load heart_scale.matmatlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2 -b 1');matlab> load heart_scale.matmatlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');Other Utilities===============A simple matlab program read_sparse.m reads files in libsvm format: [svm_lbl, svm_data] = read_sparse(fname); Two outputs are labels and instances, which can then be used as inputsof svmtrain or svmpredict. This code was initiated by Hsuan-Tien Linfrom Caltech and rewritten by Rong-En Fan from National TaiwanUniversity.Additional Information======================This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,Chih-Yuan Yang and Chih-Huai Cheng from Department of ComputerScience, National Taiwan University. The current version was preparedby Rong-En Fan. If you find this tool useful, please cite LIBSVM asfollowsChih-Chung Chang and Chih-Jen Lin, LIBSVM : a library forsupport vector machines, 2001. Software available athttp://www.csie.ntu.edu.tw/~cjlin/libsvmFor any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>.