svm-grid(1) [debian man page]

svm-grid(1)							   User Manuals 						       svm-grid(1)

NAME

       svm-grid - a parameter selection tool for LIBSVM

SYNOPSIS

       svm-grid  [-log2c  begin,end,step  ] [ -log2g begin,end,step ] [ -v fold ] [ -svmtrain pathname ] [ -gnuplot pathname ] [ -out pathname ] [
       -png pathname ] [ additional_parameters_for_svm-train ] dataset

DESCRIPTION

       grid.py is a parameter selection tool for C-SVM classification using the RBF (radial basis function) kernel. It uses cross validation  (CV)
       technique  to  estimate	the  accuracy of each parameter combination in the specified range and helps you to decide the best parameters for
       your problem.

FILES

       See svm-train(1) for the format of dataset

EXAMPLES

       svm-grid -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale

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)

Linux								     DEC 2009							       svm-grid(1)

LIBLINEAR-TRAIN(1)					      General Commands Manual						LIBLINEAR-TRAIN(1)

NAME

       liblinear-train - train a linear classifier and produce a model

SYNOPSIS

       liblinear-train [options] training_set_file [model_file]

DESCRIPTION

       liblinear-train trains a linear classifier using liblinear and produces a model suitable for use with liblinear-predict(1).

       training_set_file  is  the  file  containing  the  data	used  for  training.   model_file is the file to which the model will be saved. If
       model_file is not provided, it defaults to training_set_file.model.

       To obtain good performances, sometimes one needs to scale the data. This can be done with svm-scale(1).

OPTIONS

       A summary of options is included below.

       -s type
	      Set the type of the solver:

		 0 ... L2-regularized logistic regression

		 1 ... L2-regularized L2-loss support vector classification (dual) (default)

		 2 ... L2-regularized L2-loss support vector classification (primal)

		 3 ... L2-regularized L1-loss support vector classification (dual)

		 4 ... multi-class support vector classification

		 5 ... L1-regularized L2-loss support vector classification

		 6 ... L1-regularized logistic regression

		 7 ... L2-regularized logistic regression (dual)

       -c cost
	      Set the parameter C (default: 1)

       -e epsilon
	      Set the tolerance of the termination criterion

	      For -s 0 and 2:

		 |f'(w)|_2 <= epsilon*min(pos,neg)/l*|f'(w0)_2, where f is
		 the primal function and pos/neg are the number of positive/negative data
		 (default: 0.01)

	      For -s 1, 3, 4 and 7:

		 Dual maximal violation <= epsilon; similar to libsvm (default: 0.1)

	      For -s 5 and 6:

		 |f'(w)|_inf <= epsilon*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal
		 function (default: 0.01)

       -B bias
	      If bias >= 0, then instance x becomes [x; bias]; if bias < 0, then
	      no bias term is added (default: -1)

       -wi weight
	      Weight-adjusts the parameter C of class i by the value weight

       -v n   n-fold cross validation mode

       -q     Quiet mode (no outputs).

EXAMPLES

       Train a linear SVM using L2-loss function:

		  liblinear-train data_file

       Train a logistic regression model:

		  liblinear-train -s 0 data_file

       Do five-fold cross-validation using L2-loss SVM, using a smaller stopping tolerance 0.001 instead of the  default  0.1  for  more  accurate
       solutions:

		  liblinear-train -v 5 -e 0.001 data_file

       Train four classifiers:

			 positive    negative	     Cp  Cn
			 class 1     class 2,3,4     20  10
			 class 2     class 1,3,4     50  10
			 class 3     class 1,2,4     20  10
			 class 4     class 1,2,3     10  10

		  liblinear-train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file

       If there are only two classes, we train ONE model. The C values for the two classes are 10 and 50:

		  liblinear-train -c 10 -w3 1 -w2 5 two_class_data_file

       Output probability estimates (for logistic regression only) using liblinear-predict(1):

		  liblinear-predict -b 1 test_file data_file.model output_file

SEE ALSO

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

AUTHORS

       liblinear-train was written by the LIBLINEAR authors at National Taiwan university for the LIBLINEAR Project.

       This manual page was written by Christian Kastner <debian@kvr.at>, for the Debian project (and may be used by others).

								  March 08, 2011						LIBLINEAR-TRAIN(1)