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SVM_LEARN(1) TinySVM SVM_LEARN(1)

NAME

svm_learn - learns SVM models

SYNOPSIS

svm_learn [options] training-file model-file

DESCRIPTION

TinySVM - tiny SVM package Copyright © 2000-2002 Taku Kudo, All rights reserved.

Solver Type:

-l, --solver-type=INT
select type of solver. TYPE: 0 - C-SVM (default)
1 - C-SVR 2 - One-Class-SVM (experimental)

Kernel Parameter:

-t, --kernel-type=INT
select type of kernel function. TYPE: 0 - linear (w * x) (default)
1 - polynomial
(s w * x + r)^d
2 - neural
tanh (s w * x + r)
3 - RBF
exp (-s * ||w-x||^2)
4 - ANOVA
(sum_i [exp(-s * ||w_i-x_i||^2)])^d
-d, --kernel-degree=INT
set INT for parameter d in polynomial kernel. (default 1)
-r, --kernel-param-r=FLOAT
set FLOAT for parameter r in polynomial kernel. (default 1)
-s, --kernel-param-s=FLOAT
set FLOAT for parameter s in polynomial kernel. (default 1)

Optimization Parameter:

-m, --cache-size=FLOAT
set FLOAT for cache memory size (MB). (default 40.0)
-c, --cost=FLOAT
set FLOAT for cost C of constraints violation, trade-off between training error and margin. (default 1.0)
-e, --termination-criterion=FLOAT
set FLOAT for tolerance of termination criterion. (default 0.001)
-H, --shrinking-size=INT
set INT for number of iterations variable needs to be optimal before considered for shrinking. (default 100)
-p, --shrinking-eps=FLOAT
set FLOAT for initial threshold value of shrinking process. (default 2.0)
-f, --do-final-check=INT
do final optimality check for variables removed by shrinking. (default 1)
-i, --insensitive-loss=FLOAT
set FLOAT for epsilon in epsilon-insensitive loss function used in C-SVR cost evaluation. (default 0.1)

Miscellaneous:

-M, --model=FILE
set FILE, FILE.idx for initial condition model file.
-I, --sv-index
write all alpha and gradient to MODEL.idx.
-W, --compress
calculate vector w (w * x + b), instead of alpha.
-V, --verbose
set verbose mode.
-v, --version
show the version of TinySVM and exit.
-h, --help
show this help and exit.

TinySVM - tiny SVM package Copyright © 2000-2002 Taku Kudo, All rights reserved.

February 2013 svm_learn of 0.09