svm-easy(1) [debian man page]

svm-easy(1)							   User Manuals 						       svm-easy(1)

NAME

       svm-easy - an automatic script for LIBSVM

SYNOPSIS

       svm-easy training_file [ testing_file ]

DESCRIPTION

       svm-easy  is  an automatic script for LIBSVM. It will execute svm-scale(1) and svm-train(1) with default parameters. If the testing_file is
       specified, svm-predict(1) will also be used.

FILES

       See svm-train(1) for the format of traning_file and testing_file

EXAMPLES

	      svm-easy train.2

	      svm-easy train.3 test.3

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-train(1), svm-predict(1), svm-scale(1)

Linux								     DEC 2009							       svm-easy(1)

svm-predict(1)							   User Manuals 						    svm-predict(1)

NAME

       svm-predict - make predictions based on a trained SVM model file and test data

SYNOPSIS

       svm-predict [ -b probability_estimates ] [ -q ] test_data model_file [ output_file ]

DESCRIPTION

       svm-predict uses a Support Vector Machine specified by a given input model_file to make predictions for each of the samples in test_data
	 The format of this file is identical to the training_data file used in svm_train(1) and is just a sparse vector as follows:

       <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.  If you have label data avail-
       able for testing then you can enter these values in the test_data file.	If they are not available you can just enter 0 and will  not  know
       real accuracy for the SVM directly, however you can still get the results of its prediction for the data point.

	      If  output_file is given, it will be used to specify the filename to store the predicted results, one per line, in the same order as
	      the test_data file.

OPTIONS

       -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.

       -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 consist 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-train(1), svm-scale(1)

Linux								     MAY 2006							    svm-predict(1)