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Project Summary

The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. SHOGUN also implements a number of linear methods. It allows the input feature-objects to be dense, sparse or strings and of type int/short/double/char. It provides efficient implementations several kernels but also linear methods, hidden markov models etc. and interfaces to matlab,octave,python,R and has a cmdline interface and allows C++ extensions via a library.

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bioinformatics kernels kmeans large-scale learning machinelearning matlab matplotlib optimization python research standalone supervisedlearning supportvectormachine svm textclassification

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Languages

Languages?height=75&width=75
C++
91%
Python
6%
11 Other
3%

30 Day Summary

Aug 19 2017 — Sep 18 2017

12 Month Summary

Sep 18 2016 — Sep 18 2017
  • 819 Commits
    Down -235 (22%) from previous 12 months
  • 40 Contributors
    Up + 9 (29%) from previous 12 months