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)
