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Analyzed 3 months ago. based on code collected 3 months 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.

Tags

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...

This Project has No vulnerabilities Reported Against it

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Languages

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C++
91%
Python
5%
11 Other
4%

30 Day Summary

Mar 26 2017 — Apr 25 2017

12 Month Summary

Apr 25 2016 — Apr 25 2017
  • 1014 Commits
    Up + 206 (25%) from previous 12 months
  • 34 Contributors
    Up + 1 (3%) from previous 12 months