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svm-train(1) User Manuals svm-train(1)

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

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

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)^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) --
Sets the degree of the kernel function, defaulting to 3
Adjusts the gamma in the kernel function (default 1/k)
Sets the coef0 (constant offset) in the kernel function (default 0)
Sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and nu-SVR (default 1)
Sets the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
Set the epsilon in the loss function of epsilon-SVR (default 0.1)
Set the cache memory size to cachesize in MB (default 100)
Set the tolerance of termination criterion to epsilon (default 0.001)
Whether to use the shrinking
heuristics, 0 or 1 (default 1)
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.
Set the parameter C (cost) of class i to weight*C, for C-SVC (default 1)
Set n for n -fold cross validation mode
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> . . .

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.

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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)

MAY 2006 Linux