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-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
- 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.