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.