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Analyzed 13 days ago. based on code collected 13 days ago.

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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In a Nutshell, SHOGUN...

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Languages

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

30 Day Summary

Nov 4 2017 — Dec 4 2017

12 Month Summary

Dec 4 2016 — Dec 4 2017
  • 662 Commits
    Down -617 (48%) from previous 12 months
  • 32 Contributors
    Down -7 (17%) from previous 12 months