svmtrain libsvm Toolbox

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train

trains a linear model

Calling Sequence

model = train(training_label_vector, training_instance_matrix)
model = train(training_label_vector, training_instance_matrix, 'liblinear_options')
model = train(training_label_vector, training_instance_matrix, 'liblinear_options', 'col')

Parameters

liblinear_options:

-s type :

set type of solver (default 1)

0:

L2-regularized logistic regression (primal)

1:

L2-regularized L2-loss support vector classification (dual)

2:

L2-regularized L2-loss support vector classification (primal)

3:

L2-regularized L1-loss support vector classification (dual)

4:

multi-class support vector classification by Crammer and Singer

5:

L1-regularized L2-loss support vector classification

6:

L1-regularized logistic regression

7:

L2-regularized logistic regression (dual)

-c cost :

set the parameter C (default 1)

-e epsilon :

set tolerance of termination criterion

-s 0 and 2:

|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2

-s 1, 3, 4 and 7:

Dual maximal violation <= eps; similar to libsvm (default 0.1)

-s 5 and 6:

|f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal function (default 0.01)

-B bias :

if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)

-wi weight:

weights adjust the parameter C of different classes (see README for details)

-v n:

n-fold cross validation mode

-q :

quiet mode (no outputs)

col:

if 'col' is setted, training_instance_matrix is parsed in column format, otherwise is in row format

svmtrain libsvm Toolbox