svm-train

NAME

svm-train - train one or more SVM instance(s) on a given data set to produce a model file

SYNOPSIS

svm-train [-s svm_type ] [ -t kernel_type ] [ -d degree ] [ -g gamma ] [ -r coef0 ] [ -c cost ] [ -n nu ] [ -p epsilon ] [ -m cachesize ] [ -e epsilon ] [ -h shrinking ] [ -b probability_estimates ] ] [ -wi weight ] [ -v n ] [ -q ]

training_set_file [ model_file ]

DESCRIPTION

svm-train trains a Support Vector Machine to learn the data indicated in the training_set_file
and produce a model_file
to save the results of the learning optimization. This model can be used later with svm_predict(1) or other LIBSVM enabled software.

OPTIONS

-s svm_type

svm_type defaults to 0 and can be any value between 0 and 4 as follows:

	0		-- C-SVC
	1		-- nu-SVC
	2		-- one-class SVM
	3		-- epsilon-SVR
	4		-- nu-SVR

-t kernel_type

kernel_type defaults to 2 (Radial Basis Function (RBF) kernel) and can be any value between 0 and 4 as follows:

	0		-- linear: u.v
	1		-- polynomial: (gamma*u.v + coef0)ˆdegree
	2		-- radial basis function: exp(-gamma*|u-v|ˆ2)
	3		-- sigmoid: tanh(gamma*u.v + coef0)
	4		-- precomputed kernel (kernel values in training_set_file) --

-d degree

Sets the degree of the kernel function, defaulting to 3

-g gamma

Adjusts the gamma in the kernel function (default 1/k)

-r coef0

Sets the coef0 (constant offset) in the kernel function (default 0)

	-c cost	Sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and nu-SVR (default 1)
	-n nu	Sets the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

-p epsilon

Set the epsilon in the loss function of epsilon-SVR (default 0.1)

-m cachesize

Set the cache memory size to cachesize in MB (default 100)

-e epsilon

Set the tolerance of termination criterion to epsilon (default 0.001)

-h shrinking

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

-b probability-estimates

probability_estimates is a binary value indicating whether to calculate probability estimates when training the SVC or SVR model. Values are 0 or 1 and defaults to 0 for speed.

-wi weight

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

	-v n		Set n for n -fold cross validation mode
	-q		quiet mode; suppress messages to stdout.

FILES

training_set_file must be prepared in the following simple sparse training vector format:
<label> <index1>:<value1> <index2>:<value2> . . .

	.
	.
	.

There is one sample per line. Each sample consists of a target value
(label or regression target) followed by a sparse representation of the
input vector. All unmentioned coordinates are assumed to be 0. For
classification, <label> is an integer indicating the class label
(multi-class is supported). For regression, <label> is the target value
which can be any real number. For one-class SVM, it’s not used so can be
any number. Except using precomputed kernels (explained in another
section), <index>:<value> gives a feature (attribute) value. <index> is an
integer starting from 1 and <value> is a real number. Indices must be in an
ASCENDING order.

ENVIRONMENT

No environment variables.

DIAGNOSTICS

None documented; see Vapnik et al.

BUGS

Please report bugs to the Debian BTS.

AUTHOR

Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)

SEE ALSO

svm-predict(1), svm-scale(1)