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libsvm Toolbox >> libsvm Toolbox > svmtrain

svmtrain

trains a svm model

Calling Sequence

model = svmtrain(training_label_vector, training_instance_matrix);
model = svmtrain(training_label_vector, training_instance_matrix,libsvm_options);

Parameters

libsvm_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_instance_matrix)

-d degree :

set degree in kernel function (default 3)

-g gamma :

set gamma in kernel function (default 1/num_features)

-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 a SVC or SVR model for probability estimates, 0 or 1 (default 0)

-wi weight :

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

-v n :

n-fold cross validation mode

-q :

quiet mode (no outputs)

Description

The 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.

Scale your data. For example, scale each attribute to [0,1] or [-1,+1].

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