文件列表:

libsvm-mat-2.81-1 (0, 2005-11-22)
libsvm-mat-2.81-1\COPYRIGHT (1497, 2005-03-22)
libsvm-mat-2.81-1\heart_scale.mat (28904, 2005-03-22)
libsvm-mat-2.81-1\make.m (186, 2005-04-24)
libsvm-mat-2.81-1\Makefile (682, 2005-11-21)
libsvm-mat-2.81-1\read_sparse.m (959, 2005-04-24)
libsvm-mat-2.81-1\svm.cpp (61507, 2005-11-21)
libsvm-mat-2.81-1\svm.h (2861, 2005-04-18)
libsvm-mat-2.81-1\svmpredict.c (7686, 2005-06-09)
libsvm-mat-2.81-1\svmpredict.dll (16384, 2005-11-21)
libsvm-mat-2.81-1\svmtrain.c (9008, 2005-05-08)
libsvm-mat-2.81-1\svmtrain.dll (57344, 2005-11-21)
libsvm-mat-2.81-1\svm_model_matlab.c (6907, 2005-04-25)
libsvm-mat-2.81-1\svm_model_matlab.h (192, 2005-04-25)

----------------------------------------------- --- Document for MATLAB interface of LIBSVM --- ----------------------------------------------- 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 is 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 On Windows systems, pre-built 'svmtrain.dll' and 'svmpredict.dll' 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. 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> make Usage ===== 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_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. -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_options: 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 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 ======== matlab> load heart_scale.mat matlab> 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 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 2 -b 1'); matlab> load heart_scale.mat matlab> [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 inputs of svmtrain or svmpredict. This code was initiated by Hsuan-Tien Lin from Caltech and rewritten 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. 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 .

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