svm-landscape(1) [debian man page]

SVM-LANDSCAPE(1)						   User Commands						  SVM-LANDSCAPE(1)

NAME

       svm-landscape - Command line interface to svm-landscape in mlpy (version 2.2.0)

SYNOPSIS

       svm-landscape [options]

OPTIONS

       -h, --help
	      show this help message and exit

       -d FILE, --data=FILE
	      data - required

       -s, --standardize
	      standardize data

       -n, --normalize
	      normalize data

       -k K   k for k-fold cross validation

       -c SETS PAIRS
	      sets and pairs for monte carlo cross validation

       -S, --stratified
	      for stratified cv

       -K KERNEL, --kernel=KERNEL
	      kernel: 'linear', 'gaussian', 'polynomial', 'tr' [default linear]

       -P KPARAMETER, --kparameter=KPARAMETER
	      kernel parameter (two sigma squared) for gaussian and polynomial kernels [default 0.1]

       -o COST, --cost=COST
	      for cost-sensitive classification [-1.0, 1.0] [default 0.0]

       -m MIN, --min=MIN
	      min value for regularization parameter [default -5]

       -M MAX, --max=MAX
	      max value for regularization parameter [default 5]

       -p STEPS, --steps=STEPS
	      steps for regularization parameter [default 11]

       -e SCALE, --scale=SCALE
	      scale for regularization parameter: 'lin' or 'log' [default log]

       -l, --lists
	      Canberra distance indicator

       -a, --auc
	      Wmw_auc metric computation

mlpy								     July 2010							  SVM-LANDSCAPE(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)