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Document: shogun
Title: Software for Semidefinite Programming
Author: Soeren Sonnenburg, Gunnar Raetsch
Abstract: SHOGUN - is a new machine learning toolbox with focus on large scale
 kernel methods and especially on Support Vector Machines (SVM) applied to the
 field of 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.
Section: Science/Data Analysis

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