Sören Sonnenburg
email:soeren (dot) sonnenburg (at) first (dot) fraunhofer (dot) de
organization:Fraunhofer FIRST, http://www.first.fraunhofer.de/
phone:+49 (30) 6392 1882
homepage:http://ida.first.fraunhofer.de/homepages/sonne/first/
search externally: Google Schoolar, CiteSeer, Live Search Academic, DBlife, Scirus

Description

The Journal of Machine Learning Research will now publish machine learning open source software (MLOSS). Complementing this effort we have been setting up mloss.org - a portal for any kind of machine learning open source software and ask you to contribute.

Software: I am the main author of the SHOGUN machine learning toolbox which started with a Hidden Markov Model to be used for Splice Site Classification back in 1999 and was since then continuously extended by Gunnar Rätsch and me. Now its focus is on large scale kernel methods, especially Support Vector Machines. 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.
SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python. A more exhaustive feature list can be found on its project page: http:www.shogun-toolbox.org
http://www.shogun-toolbox.org/

Lectures:
Sören Sonnenburg Sören Sonnenburg
Large Scale Learning - Challenge: Discussion and Summary

as author at Workshops,
4 views
Sören Sonnenburg Interior Point SVM

as author at Workshops,
8 views
Sören Sonnenburg Large Scale Learning - Challenge

as author at Workshops,
together with: Vojtech Franc,
11 views
Sören Sonnenburg Challenge with Large Scale Problems

as author at Pascal Symposium meeting,
45 views
Sören Sonnenburg Machine learning open source software

as author at Pascal Symposium meeting,
60 views
Sören Sonnenburg Large Scale Learning with String Kernels

as author at NIPS '07 Workshop on Efficient Machine Learning,
100 views
Sören Sonnenburg Learning with Millions of Examples and Dimensions - Competition proposal

as author at NIPS '07 Workshop on Efficient Machine Learning,
42 views
Sören Sonnenburg Large Scale Genomic Sequence Support Vector Machines

as author at Neural Information Processing Systems - NIPS05 Workshops,
21 views
Sören Sonnenburg Learning interpretable SVMs for biological sequence classification

as author at Machine Learning, Support Vector Machines, and Large Scale Optimization Workshop,
38 views