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

       -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)

Linux								     MAY 2006							      svm-train(1)