Name

svmtrain — Train data.

Calling Sequence

model=svmtrain(training_label_vector, training_instance_matrix [, libsvm_options])

Parameters

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 character string of training 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)
4 - precomputed kernel (kernel values in training_set_file)
-d degree

set degree in kernel function (default 3)

-g gamma

set gamma in kernel function (default 1/k). The k means the number of attributes in the input data

-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 100)

-e epsilon

set tolerance of termination criterion (default 0.001)

-h shrinking

whether to use the shrinking heuristics, 0 or 1 (default 1)

-b probability_estimates

whether to train an SVC or SVR model for probability estimates, 0 or 1 (default 0)

-wi weight

set the parameter C of class i to weight*C in C-SVC (default 1)

-v n

n-fold cross validation mode. option -v randomly splits the data into n parts and calculates cross validation accuracy/mean squared error on them

model

The returned model structure for future prediction by 'svmtrain'. It is a structure 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

Description

Train data and generate a 'Modle' file which could be thought as a storage format for the internal data of SVM.

Examples

       [label_vector, instance_vector] = readsparse('heart_scale');
       modle = svmtrain(label_vector, instance_vector, '-c 1 -g 0.07')

Authors

Chih-Jen Lin <cjlin@csie.ntu.edu.tw>
Chih-Chung Chang
Jun-Cheng Chen
Kuan-Jen Peng
Chih-Yuan Yang
Chih-Huai Cheng
Rong-En Fan
Ting-Fan Wu
Jia Wu <jiawu83@gmail.com>

Bibliography

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.

Availability

The latest version of OpenPR can be found at http://www.openpr.org.cn.

See Also

svmpredict, readsparse