svm-grid(1) [debian man page]

svm-grid(1)							   User Manuals 						       svm-grid(1)

NAME

       svm-grid - a parameter selection tool for LIBSVM

SYNOPSIS

       svm-grid  [-log2c  begin,end,step  ] [ -log2g begin,end,step ] [ -v fold ] [ -svmtrain pathname ] [ -gnuplot pathname ] [ -out pathname ] [
       -png pathname ] [ additional_parameters_for_svm-train ] dataset

DESCRIPTION

       grid.py is a parameter selection tool for C-SVM classification using the RBF (radial basis function) kernel. It uses cross validation  (CV)
       technique  to  estimate	the  accuracy of each parameter combination in the specified range and helps you to decide the best parameters for
       your problem.

FILES

       See svm-train(1) for the format of dataset

EXAMPLES

       svm-grid -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale

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-train(1), svm-predict(1)

Linux								     DEC 2009							       svm-grid(1)

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)