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

-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)^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
    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)