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  The Need for Open Source Software in Machine Learning

Sonnenburg, S., Braun, M., Ong, C., Bengio, S., Bottou, L., Holmes, G., et al. (2007). The Need for Open Source Software in Machine Learning. The Journal of Machine Learning Research, 8, 2443-2466.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CB87-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-BBBB-8
Genre: Journal Article

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 Creators:
Sonnenburg, S, Author              
Braun, ML, Author
Ong, CS1, Author              
Bengio, S, Author
Bottou, L, Author
Holmes , G, Author
LeCun, Y, Author
Müller, K-R, Author              
Pereira, F, Author
Rasmussen, CE, Author              
Rätsch, G1, Author              
Schölkopf, B2, 3, Author              
Smola, A, Author
Vincent, P, Author
Weston, J, Author
Williamson, RC, Author
Affiliations:
1Friedrich Miescher Laboratory, Max Planck Society, ou_2575692              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not realized, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.

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 Dates: 2007-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4768
 Degree: -

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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 2443 - 2466 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1