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Special Forums News, Links, Events and Announcements Software Releases - RSS News SHOGUN 0.6.4 (Default branch)
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Old 08-15-2008
SHOGUN 0.6.4 (Default branch)
ImageSHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.License: GNU General Public License (GPL)Changes:
This release contains major feature enhancements and bugfixes. It implements 2-norm Multiple Kernel Learning, has greatly extended documentation, adds a Gaussian kernel for 32-bit floating point features, and implements the test suite for most of the functions for most interfaces. It also fixes a bug in filtering out duplicate signals in the signal handler, and fixes random number generator initialization.Image

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LEARN ABOUT DEBIAN

linclass

LINCLASS(1)						      General Commands Manual						       LINCLASS(1)

NAME

       linclass - predict labels by a linear classification rule

SYNOPSIS

       linclass [options] example_file model_file

DESCRIPTION

       linclass is a program that predicts labels by a linear classification rule.

       example_file  is  a  file  with testing examples in SVM^light format, and model_file is the file which contains either a binary (two-class)
       rule f(x)=w'*x+w0 or a multi-class rule f(x)=W'*x. These are produced svmocas(1) and msvmocas(1), respectively.

OPTIONS

       A summary of options is included below.

       -h     Show summary of options.

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

       -e     Print the classification error computed from predicted labels and labels contained in example_file.

       -o out_file
	      Save predictions to the file out_file.

       -t (0|1)
	      Output type:

		   0 ... predicted labels (default)

		   1 ... discriminant values

EXAMPLES

       Train the multi-class SVM classifier from example file fiply_trn.light, using svmocas(1) with the regularization constant  C=10,  verbosity
       switched off, and save model to svmocas.model:

		    svmocas -c 10 -b 1 -v 0 riply_trn.light svmocas.model

       Compute	the  testing  error  of  the classifier stored in svmocas.model using testing examples from riply_tst.light and save the predicted
       labels to riply_tst.pred:

		    linclass -e -o riply_tst.pred riply_tst.light svmocas.model

SEE ALSO

       svmocas(1), msvmocas(1).

AUTHORS

       linclass 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						       LINCLASS(1)