Libsvm is a simple, easy-to-use, and efficient software for SVMclassification and regression. It can solve C-SVM classification,nu-SVM classification, one-class-SVM, epsilon-SVM regression,and nu-SVM regression. This document explains the use of libsvm.Libsvm is available at http://www.csie.ntu.edu.tw/~cjlin/libsvmPlease read the COPYRIGHT file before using libsvm.Installation============On Unix systems, type `make' to build the `svm-train' and `svm-predict'programs. Run them without arguments to show the usages of them.On other systems, consult `Makefile' to build them or use the pre-builtbinaries (Windows binaries are in the subdirectory `windows').The format of training and testing data file is:<label> <index1>:<value1> <index2>:<value2> ......<label> is the target value of the training data. For classification,it should be an integer which identifies a class (multi-class classificationis supported). For regression, it's any real number. For one-class SVM,it's not used so can be any number. <index> is an integer starting from 1,<value> is a real number. The labels in the testing data file are only used tocalculate accuracy or error. If they are unknown, just fill this column with anumber.There is a sample training data for classification in this package:heart_scale.Type `svm-train heart_scale', and the program will read the trainingdata and output the model file `heart_scale.model'. Then you cantype `svm-predict heart_scale heart_scale.model output' to see therate of classification on training data. The `output' file containsthe prediction value of the model.There are some other useful programs in this package.svm-scale:	This is a tool for scaling input data file.svm-toy:	This is a simple graphical interface which shows how SVM	separate data in a plane. You can click in the window to 	draw data points. Use "change" button to choose class 	1 or 2, "load" button to load data from a file, "save" button	to save data to a file, "run" button to obtain an SVM model,	and "clear" button to clear the window.	You can enter options in the bottom of the window, the syntax of	options is the same as `svm-train'.	Note that "load" and "save" consider data in the classification but	not the regression case. Each data point has one label (the color)	and two attributes (x-axis and y-axis values).	Type `make' in respective directories to build them.	You need Qt library to build the Qt version.	(You can download it from http://www.trolltech.com)	You need GTK+ library to build the GTK version.	(You can download it from http://www.gtk.org)		We use Visual C++ to build the Windows version.	The pre-built Windows binaries are in the windows subdirectory.`svm-train' Usage=================Usage: svm-train [options] training_set_file [model_file]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)-d degree : set degree in kernel function (default 3)-g gamma : set gamma in kernel function (default 1/k)-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 40)-e epsilon : set tolerance of termination criterion (default 0.001)-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)-wi weight: set the parameter C of class i to weight*C in C-SVC (default 1)-v n: n-fold cross validation modeThe k in the -g option means the number of attributes in the input data.option -v randomly splits the data into n parts and calculates crossvalidation accuracy/mean squared error on them.`svm-predict' Usage===================Usage: svm-predict test_file model_file output_filemodel_file is the model file generated by svm-train.test_file is the test data you want to predict.svm-predict will produce output in the output_file.No options are needed for svm-predict.Tips on practical use=====================* Scale your data. For example, scale each attribute to [0,1] or [-1,+1].* For C-SVC, try small and large C, like 1 to 1000 and decide which are  better for your data by cross validation. For the better C's, try  several gamma's.* nu in nu-SVC/one-class-SVM/nu-SVR approximates the fraction of training  errors and support vectors.* If data for classification are unbalanced (e.g. many positive and  few negative), try different penalty parameters C by -wi (see  examples below).Examples========> svm-train -s 0 -c 1000 -t 1 -g 1 -r 1 -d 3 data_fileTrain a classifier with polynomial kernel (u'v+1)^3 and C = 1000> svm-train -s 1 -n 0.1 -t 2 -g 0.5 -e 0.00001 data_fileTrain a classifier by nu-SVM (nu = 0.1) with RBF kernelexp(-0.5|u-v|^2) and stopping tolerance 0.00001> svm-train -s 3 -p 0.1 -t 0 -c 10 data_fileSolve SVM regression with linear kernel u'v and C=10, and epsilon = 0.1in the loss function.> svm-train -s 0 -c 10 -w1 1 -w-1 5 data_fileTrain a classifier with penalty 10 for class 1 and penalty 50for class -1.> svm-train -s 0 -c 500 -g 0.1 -v 5 data_fileDo five-fold cross validation for the classifier usingthe parameters C = 500 and gamma = 0.1Library Usage=============These functions and structures are declared in the header file `svm.h'.You need to #include "svm.h" in your C/C++ source files and link yourprogram with `svm.cpp'. You can see `svm-train.c' and `svm-predict.c'for examples showing how to use them.Before you classify test data, you need to construct an SVM model(`svm_model') using training data. A model can also be saved ina file for later use. Once an SVM model is available, you can use itto classify new data.- Function: struct svm_model *svm_train(const struct svm_problem *prob,					const struct svm_parameter *param);    This function constructs and returns an SVM model according to    the given training data and parameters.    struct svm_problem describes the problem:		struct svm_problem	{		int l;		double *y;		struct svm_node **x;	};     where `l' is the number of training data, and `y' is an array containing    their target values. (integers in classification, real numbers in    regression) `x' is an array of pointers, each of which points to a sparse    representation (array of svm_node) of one training vector.    For example, if we have the following training data:    LABEL	ATTR1	ATTR2	ATTR3	ATTR4	ATTR5    -----	-----	-----	-----	-----	-----      1		  0	  0.1	  0.2	  0	  0      2		  0	  0.1	  0.3	 -1.2	  0      1		  0.4	  0	  0	  0	  0      2		  0	  0.1	  0	  1.4	  0.5      3		 -0.1	 -0.2	  0.1	  1.1	  0.1    then the components of svm_problem are:    l = 5    y -> 1 2 1 2 3    x -> [ ] -> (2,0.1) (3,0.2) (-1,?)	 [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)	 [ ] -> (1,0.4) (-1,?)	 [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)	 [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)    where (index,value) is stored in the structure `svm_node':	struct svm_node	{		int index;		double value;	};    index = -1 indicates the end of one vector.     struct svm_parameter describes the parameters of an SVM model:	struct svm_parameter	{		int svm_type;		int kernel_type;		double degree;	// for poly		double gamma;	// for poly/rbf/sigmoid		double coef0;	// for poly/sigmoid		// these are for training only		double cache_size; // in MB		double eps;	// stopping criteria		double C;	// for C_SVC, EPSILON_SVR, and NU_SVR		int nr_weight;		// for C_SVC		int *weight_label;	// for C_SVC		double* weight;		// for C_SVC		double nu;	// for NU_SVC, ONE_CLASS, and NU_SVR		double p;	// for EPSILON_SVR		int shrinking;	// use the shrinking heuristics	};    svm_type can be one of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR.    C_SVC:		C-SVM classification    NU_SVC:		nu-SVM classification    ONE_CLASS:		one-class-SVM    EPSILON_SVR:	epsilon-SVM regression    NU_SVR:		nu-SVM regression    kernel_type can be one of LINEAR, POLY, RBF, SIGMOID.    LINEAR:	u'*v    POLY:	(gamma*u'*v + coef0)^degree    RBF:	exp(-gamma*|u-v|^2)    SIGMOID:	tanh(gamma*u'*v + coef0)    cache_size is the size of the kernel cache, specified in megabytes.    C is the cost of constraints violation. (we usually use 1 to 1000)    eps is the stopping criterion. (we usually use 0.00001 in nu-SVC,    0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and    one-class-SVM. p is the epsilon in epsilon-insensitive loss function    of epsilon-SVM regression. shrinking = 1 means shrinking is conducted;    = 0 otherwise.    nr_weight, weight_label, and weight are used to change the penalty    for some classes (If the weight for a class is not changed, it is    set to 1). This is useful for training classifier using unbalanced    input data or with asymmetric misclassification cost.    nr_weight is the number of elements in the array weight_label and    weight. Each weight[i] corresponds to weight_label[i], meaning that    the penalty of class weight_label[i] is scaled by a factor of weight[i].        If you do not want to change penalty for any of the classes,    just set nr_weight to 0.    *NOTE* Because svm_model contains pointers to svm_problem, you can    not free the memory used by svm_problem if you are still using the    svm_model produced by svm_train().- Function: double svm_predict(const struct svm_model *model,                             const struct svm_node *x);    This function does classification or regression on a test vector x    given a model.    For a classification model, the predicted class for x is returned.    For a regression model, the function value of x calculated using    the model is returned. For one-class model, +1 or -1 is returned.- Function: int svm_save_model(const char *model_file_name,			       const struct svm_model *model);    This function saves a model to a file; returns 0 on success, or -1    if an error occurs.- Function: struct svm_model *svm_load_model(const char *model_file_name);    This function returns a pointer to the model read from the file,    or a null pointer if the model could not be loaded.- Function: void svm_destroy_model(struct svm_model *model);    This function frees the memory used by a model.Java version============The precompiled java class archive `libsvm.jar' and its source files arein the java subdirectory. To run the programs, usejava -classpath libsvm.jar svm_train <arguments>java -classpath libsvm.jar svm_predict <arguments>java -classpath libsvm.jar svm_toyWe have tried IBM's and Sun's JDK.You may need to add Java runtime library (like classes.zip) to the classpath.You may need to increase maximum Java heap size.Library usages are similar to the C version. These functions are available:public class svm {	public static svm_model svm_train(svm_problem prob, svm_parameter param);	public static double svm_predict(svm_model model, svm_node[] x);	public static void svm_save_model(String model_file_name, svm_model model) throws IOException	public static svm_model svm_load_model(String model_file_name) throws IOException}The library is in the "libsvm" package.Note that in Java version, svm_node[] is not ended with a node whose index = -1.ADDITIONAL INFORMATION============Chih-Chung Chang and Chih-Jen LinLIBSVM : a library for support vector machines.http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gzAcknowledgments:This work was supported in part by the National Science Council of Taiwan via the grant NSC 89-2213-E-002-013.The authors thank Chih-Wei Hsu and Jen-Hao Leefor many helpful discussions and comments.