svm-train
Section: User Manuals (1)
Updated: MAY 2006
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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)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- OPTIONS
-
- FILES
-
- ENVIRONMENT
-
- DIAGNOSTICS
-
- BUGS
-
- AUTHOR
-
- SEE ALSO
-
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