GNU/Linux man pages
Livre :
Expressions régulières,
Syntaxe et mise en oeuvre :
GNU/Linux |
Debian 7.2.0(Wheezy) |
amd64 |
 |
svm-train(1) |
 |
svm-train − train one or more SVM instance(s) on a given data set to produce a model file
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 ]
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.
-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. |
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.
No environment variables.
None documented; see Vapnik et al.
Please report bugs to the Debian BTS.
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)
svm-predict(1), svm-scale(1)
|
svm-train(1) | |