文件列表:

SVMSezDetCode1 (0, 2010-05-08)
SVMSezDetCode1\.DS_Store (6148, 2010-05-08)
__MACOSX (0, 2010-05-08)
__MACOSX\SVMSezDetCode1 (0, 2010-05-08)
__MACOSX\SVMSezDetCode1\._.DS_Store (82, 2010-05-08)
SVMSezDetCode1\CalcSensSpecDel.m (4091, 2010-05-08)
__MACOSX\SVMSezDetCode1\._CalcSensSpecDel.m (197, 2010-05-08)
SVMSezDetCode1\crossValidation.m (2285, 2010-05-08)
__MACOSX\SVMSezDetCode1\._crossValidation.m (197, 2010-05-08)
SVMSezDetCode1\ExtractFeatures.m (4453, 2010-05-08)
__MACOSX\SVMSezDetCode1\._ExtractFeatures.m (197, 2010-05-08)
SVMSezDetCode1\FindWvltMaxFreq1Wv.m (1495, 2010-05-08)
__MACOSX\SVMSezDetCode1\._FindWvltMaxFreq1Wv.m (197, 2010-05-08)
SVMSezDetCode1\LIBSVMx (0, 2010-05-08)
SVMSezDetCode1\LIBSVMx\.DS_Store (6148, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx (0, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._.DS_Store (82, 2010-05-08)
SVMSezDetCode1\LIBSVMx\COPYRIGHT (1497, 2008-04-02)
__MACOSX\SVMSezDetCode1\LIBSVMx\._COPYRIGHT (197, 2008-04-02)
SVMSezDetCode1\LIBSVMx\make.m (288, 2009-02-25)
__MACOSX\SVMSezDetCode1\LIBSVMx\._make.m (197, 2009-02-25)
SVMSezDetCode1\LIBSVMx\Makefile (894, 2008-10-27)
__MACOSX\SVMSezDetCode1\LIBSVMx\._Makefile (197, 2008-10-27)
SVMSezDetCode1\LIBSVMx\read_sparse.c (4029, 2008-10-20)
__MACOSX\SVMSezDetCode1\LIBSVMx\._read_sparse.c (197, 2008-10-20)
SVMSezDetCode1\LIBSVMx\svm.cpp (92045, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svm.cpp (197, 2010-05-08)
SVMSezDetCode1\LIBSVMx\svm.h (3149, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svm.h (197, 2010-05-08)
SVMSezDetCode1\LIBSVMx\svm_model_matlab.c (8750, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svm_model_matlab.c (197, 2010-05-08)
SVMSezDetCode1\LIBSVMx\svm_model_matlab.h (203, 2007-11-23)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svm_model_matlab.h (197, 2007-11-23)
SVMSezDetCode1\LIBSVMx\svmpredict.c (9066, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svmpredict.c (197, 2010-05-08)
SVMSezDetCode1\LIBSVMx\svmtrain.c (11417, 2010-05-08)
__MACOSX\SVMSezDetCode1\LIBSVMx\._svmtrain.c (197, 2010-05-08)
__MACOSX\SVMSezDetCode1\._LIBSVMx (197, 2010-05-08)
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----------------------------------------- --- MATLAB/OCTAVE interface of LIBSVM --- ----------------------------------------- Table of Contents ================= - Introduction - Installation - Usage - Returned Model Structure - Examples - Other Utilities - Additional Information Introduction ============ This tool provides a simple interface to LIBSVM, a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as the usage and the way of specifying parameters are the same as that of LIBSVM. Installation ============ On Unix systems, we recommend using GNU g++ as your compiler and type '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> make To use Octave, type 'make octave': Example: linux> make octave On Windows systems, pre-built 'svmtrain.mexw32' and 'svmpredict.mexw32' are included in this package, so no need to conduct installation. If you have 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. Starting from MATLAB 7.1 (R14SP3), the default MEX file extension is changed from .dll to .mexw32 or .mexw*** (depends on 32-bit or ***-bit Windows). If your MATLAB is older than 7.1, you have to build these files yourself. 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 7.1 in C:\Program Files\Microsoft Visual Studio [0] None Compiler: 1 Please verify your choices: Compiler: Microsoft Visual C/C++ 7.1 Location: C:\Program Files\Microsoft Visual Studio Are these correct?([y]/n): y matlab> make Under ***-bit Windows, Visual Studio 2005 user will need "X*** Compiler and Tools". The package won't be installed by default, but you can find it in customized installation options. For list of supported/compatible compilers for MATLAB, please check the following page: http://www.mathworks.com/support/compilers/current_release/ Usage ===== matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']); -training_label_vector: An m by 1 vector of training labels (type must be double). -training_instance_matrix: An m by n matrix of m training instances with n features. It can be dense or sparse (type must be double). -libsvm_options: A string of training options in the same format as that of LIBSVM. matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']); -testing_label_vector: An m by 1 vector of prediction labels. If labels of test data are unknown, simply use any random values. (type must be double) -testing_instance_matrix: An m by n matrix of m testing instances with n features. It can be dense or sparse. (type must be double) -model: The output of svmtrain. -libsvm_options: A string of testing options in the same format as that of LIBSVM. A few of the frequently used options are -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 5 -- SVDD -g gamma : set gamma in kernel function (default 1/num_features) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) Returned Model Structure ======================== The 'svmtrain' function returns a model which can be used for future prediction. 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 vectors If you do not use the option '-b 1', ProbA and ProbB are empty matrices. If the '-v' option is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy 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 LIBSVM implementation document (http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf). Result of Prediction ==================== The function 'svmpredict' has three outputs. The first one, predictd_label, is a vector of predicted labels. The second output, accuracy, is a vector including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression). The third is a matrix containing decision values or probability estimates (if '-b 1' is specified). If k is the number of classes, for decision values, each row includes results of predicting k(k-1/2) binary-class SVMs. For probabilities, each row contains k values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as 'Label' field in the model structure. Examples ======== Train and test on the provided data heart_scale: matlab> load heart_scale.mat matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07'); matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data For probability estimates, you need '-b 1' for training and testing: matlab> load heart_scale.mat matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1'); matlab> load heart_scale.mat matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1'); To use precomputed kernel, you must include sample serial number as the first column of the training and testing data (assume your kernel matrix is K, # of instances is n): matlab> K1 = [(1:n)', K]; % include sample serial number as first column matlab> model = svmtrain(label_vector, K1, '-t 4'); matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data Take linear kernel for example, the following precomputed kernel example gives exactly same training error as LIBSVM built-in linear kernel matlab> load heart_scale.mat matlab> n = size(heart_scale_inst,1); matlab> K = heart_scale_inst*heart_scale_inst'; matlab> K1 = [(1:n)', K]; matlab> model = svmtrain(heart_scale_label, K1, '-t 4'); matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, K1, model); Note that for testing, you can put anything in the testing_label_vector. For details of precomputed kernels, please read the section ``Precomputed Kernels'' in the README of the LIBSVM package. Other Utilities =============== A matlab function read_sparse reads files in LIBSVM format: [label_vector, instance_matrix] = read_sparse('data.txt'); Two outputs are labels and instances, which can then be used as inputs of svmtrain or svmpredict. This code is derived from svm-train.c in LIBSVM by Rong-En Fan from National Taiwan University. Additional Information ====================== This 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, 2001. 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

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