SVM_light

source:http://svmlight.joachims.org/

svm_light/svm_learn -v 1 -t 0 -c 0.01 FeatureTrain.txt svmLightModel.txt

SVMlight consists of a learning module (svm_learn) and a classification module (svm_classify).

svm_learn is called with the following parameters:

svm_learn [options] example_file model_file

options:
General options:
-v [0..3] – verbosity level (default 1)
Kernel options:
-t int – type of kernel function:
0: linear (default)
1: polynomial (s a*b+c)^d
2: radial basis function exp(-gamma ||a-b||^2)
3: sigmoid tanh(s a*b + c)
4: user defined kernel from kernel.h
Learning options:
-z {c,r,p} – select between classification (c), regression (r), and
preference ranking (p) (see [Joachims, 2002c])
(default classification)
-c float – C: trade-off between training error and margin (default [avg. x*x]^-1)
In classification mode, the target value denotes the class of the example. +1 as the target value marks a positive example, -1 a negative example respectively.
The model is written to model_file.

svm_light/svm_classify FeatureTest.txt svmLightModel.txt FeaturePredict.txt 

To make predictions on test examples, svm_classify reads this file. svm_classify is called with the following parameters:

svm_classify [options] example_file model_file output_file

Available options are:

-h Help.
-v [0..3] Verbosity level (default 2).
-f [0,1] 0: old output format of V1.0
1: output the value of decision function (default)

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