文件列表:

libsvm-mat-2.88-1\svmtrain.mexw32 (65536, 2008-10-31)
libsvm-mat-2.88-1\svmtrain.c (10825, 2008-10-14)
libsvm-mat-2.88-1\svm.cpp (61809, 2008-10-31)
libsvm-mat-2.88-1\svm.h (2899, 2008-10-31)
libsvm-mat-2.88-1\svm_model_matlab.c (7655, 2008-10-31)
libsvm-mat-2.88-1\svm_model_matlab.h (201, 2007-11-23)
libsvm-mat-2.88-1\heart_scale.mat (28904, 2005-03-22)
libsvm-mat-2.88-1\COPYRIGHT (1497, 2008-04-02)
libsvm-mat-2.88-1\svmpredict.mexw32 (32768, 2008-10-31)
libsvm-mat-2.88-1\svmpredict.c (8488, 2008-10-14)
libsvm-mat-2.88-1\make.m (208, 2006-11-28)
libsvm-mat-2.88-1\read_sparse.mexw32 (8192, 2008-10-31)
libsvm-mat-2.88-1\read_sparse.c (3829, 2008-10-19)
libsvm-mat-2.88-1\Makefile (1311, 2008-06-06)
libsvm-mat-2.88-1\heart.dat (16461, 2008-12-19)
libsvm-mat-2.88-1\libsvm_heart.m (10221, 2009-01-09)
libsvm-mat-2.88-1\Scale.m (504, 2002-01-28)
libsvm-mat-2.88-1\libsvm_pima_indians_diabetes_data.m (10248, 2009-01-09)
libsvm-mat-2.88-1\pima_indians_diabetes_data.dat (24047, 2008-12-19)
libsvm-mat-2.88-1\RL_libsvm_heart.m (4837, 2009-01-09)
libsvm-mat-2.88-1\RL_libsvm_pima_indians_diabetes_data.m (4925, 2009-01-09)
libsvm-mat-2.88-1\libsvm_ionosphere.m (10843, 2009-01-09)
libsvm-mat-2.88-1\RL_libsvm_ionosphere.m (5496, 2009-01-09)
libsvm-mat-2.88-1\TCMKNN_pima.m (12581, 2009-01-04)
libsvm-mat-2.88-1\haberman_data.dat (3409, 2008-12-19)
libsvm-mat-2.88-1\TCMKNN_haberman.m (12478, 2009-01-04)
libsvm-mat-2.88-1\libsvm_haberman.m (10214, 2009-01-09)
libsvm-mat-2.88-1\german_data_numeric.dat (103000, 2008-12-19)
libsvm-mat-2.88-1\libsvm_german_data_numeric.m (10265, 2009-01-09)
libsvm-mat-2.88-1\RL_libsvm_german.m (4916, 2009-01-09)
libsvm-mat-2.88-1\RL_libsvm_haberman.m (4801, 2009-01-09)
libsvm-mat-2.88-1\libsvm_iris.m (10119, 2009-01-09)
libsvm-mat-2.88-1\RL_libsvm_iris.m (4842, 2009-01-09)
libsvm-mat-2.88-1\iris.dat (1902, 2009-01-06)
libsvm-mat-2.88-1\tu_heart.m (3454, 2009-01-06)
libsvm-mat-2.88-1\tu_german.m (7174, 2009-01-06)
libsvm-mat-2.88-1\tu_haberman.m (1903, 2009-01-07)
libsvm-mat-2.88-1 (0, 2008-11-02)

----------------------------------------- --- 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. 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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