svm-train - Online in the Cloud

PROGRAM:

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.

Use svm-train online using onworks.net services