msvmocas(1) [debian man page]

MSVMOCAS(1)						      General Commands Manual						       MSVMOCAS(1)

NAME

       msvmocas - train a multi-class linear SVM classifier

SYNOPSIS

       msvmocas [options] example_file model_file

DESCRIPTION

       msvmocas  is  a	program  that  trains  a  multi-class linear SVM classifier using the Optimized Cutting Plane Algorithm for Support Vector
       Machines (OCAS) and produces a model file.

       example_file is a file with training examples in SVM^light format, and model_file is the file in which to store	the  learned  linear  rule
       f(x)=W'*x. model_file contains M columns and D lines, where M is the number of classes and D the number of dimensions, corresponding to the
       elements of the matrix W [D x M].

OPTIONS

       A summary of options is included below.

       General options:

       -h     Show summary of options.

       -v (0|1)
	      Set the verbosity level (default: 1)

       Learning options:

       -c float
	      Regularization constant C. (default: 1)

       -n integer
	      Use only the first integer examples for training. By default, integer equals the number of examples in example_file.

       Optimization options:

       -m (0|1)
	      Solver to be used:

		   0 ... standard cutting plane (equivalent to BMRM, SVM^perf)

		   1 ... OCAS (default)

       -s integer
	      Cache size for cutting planes. (default: 2000)

       Stopping conditions:

       -a float
	      Absolute tolerance TolAbs: halt if QP-QD <= TolAbs. (default: 0)

       -r float
	      Relative tolerance TolAbs: halt if QP-QD <= abs(QP)*TolRel.  (default: 0.01)

       -q float
	      Desired objective value QPValue: halt is QP <= QPValue. (default: 0)

       -t float
	      Halts if the solver time (loading time is not counted) exceeds the time given in seconds. (default: infinity)

EXAMPLES

       Train the multi-class SVM classifier from example file example4_train.light, with the regularization constant C=10, verbosity switched off,
       and save model to msvmocas.model:

		    msvmocas -c 10 -v 0 example4_train.light msvmocas.model

       Compute	the  testing error of the classifier stored in msvmocas.model with linclass(1) using testing examples from example4_test.light and
       save the predicted labels to example4_test.pred:

		    linclass -e -o example4_test.pred example4_test.light msvmocas.model

SEE ALSO

       svmocas(1), linclass(1).

AUTHORS

       msvmocas was written by Vojtech Franc <xfrancv@cmp.felk.cvut.cz> and Soeren Sonnenburg <Soeren.Sonnenburg@tu-berlin.de>.

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

								   June 16, 2010						       MSVMOCAS(1)

OPENCV_PERFORMANCE(1)						   User Commands					     OPENCV_PERFORMANCE(1)

NAME

       opencv_performance - evaluate the performance of the classifier

SYNOPSIS

       opencv_performance [options]

DESCRIPTION

       opencv_performance  evaluates the performance of the classifier. It takes a collection of marked up test images, applies the classifier and
       outputs the performance, i.e. number of found objects, number of missed objects, number of false alarms and other information.

       When there is no such collection available test samples may be created from single object image by the opencv_createsamples(1) utility. The
       scheme of test samples creation in this case is similar to training samples

       In the output, the table should be read:

       'Hits' shows the number of correctly found objects

       'Missed'
	      shows the number of missed objects (must exist but are not found, also known as false negatives)

       'False'
	      shows the number of false alarms (must not exist but are found, also known as false positives)

OPTIONS

       opencv_performance supports the following options:

       -data classifier_directory_name
	      The directory, in which the classifier can be found.

       -info collection_file_name
	      File with test samples description.

       -maxSizeDiff max_size_difference
	      Determine the size criterion of reference and detected coincidence.  The default is 1.500000.

       -maxPosDiff max_position_difference
	      Determine the position criterion of reference and detected coincidence.  The default is 0.300000.

       -sf scale_factor
	      Scale the detection window in each iteration. The default is 1.200000.

       -ni    Don't save detection result to an image. This could be useful, if collection_file_name contains paths.

       -nos number_of_stages
	      Number of stages to use. The default is -1 (all stages are used).

       -rs roc_size
	      The default is 40.

       -h sample_height
	      The sample height (must have the same value as used during creation).  The default is 24.

       -w sample_width
	      The sample width (must have the same value as used during creation).  The default is 24.

       The same information is shown, if opencv_performance is called without any arguments/options.

EXAMPLES

       To create training samples from one image applying distortions and show the results:

	      opencv_performance -data trainout -info tests.dat

SEE ALSO

       opencv_createsamples(1), opencv_haartraing(1)

       More information and examples can be found in the OpenCV documentation.

AUTHORS

       This  manual page was written by Daniel Leidert <daniel.leidert@wgdd.de> and Nobuhiro Iwamatsu <iwamatsu@debian.org> for the Debian project
       (but may be used by others).

OpenCV								     May 2010						     OPENCV_PERFORMANCE(1)