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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | Package: elwms
Version: 1.1.0
Date: 2012-02-16 21:03:35
Title: The SHOGUN Machine Learning Toolbox
Author: Soeren Sonnenburg, Gunnar Raetsch
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Depends: R (>= 2.10.0)
Suggests:
Description: SHOGUN - is a new machine learning toolbox with focus on large
scale kernel methods and especially on Support Vector Machines (SVM) with focus
to bioinformatics. It provides a generic SVM object interfacing to several
different SVM implementations. Each of the SVMs can be combined with a variety
of the many kernels implemented. It can deal with weighted linear combination
of a number of sub-kernels, each of which not necessarily working on the same
domain, where an optimal sub-kernel weighting can be learned using Multiple
Kernel Learning. Apart from SVM 2-class classification and regression
problems, a number of linear methods like Linear Discriminant Analysis (LDA),
Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
train hidden markov models are implemented. The input feature-objects can be
dense, sparse or strings and of type int/short/double/char and can be converted
into different feature types. Chains of preprocessors (e.g. substracting the
mean) can be attached to each feature object allowing for on-the-fly
pre-processing.
License: GPL Version 3 or later.
URL: http://www.shogun-toolbox.org
Built: 2.14.1; x86_64-pc-linux-gnu; unix;
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