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  The Need for Open Source Software in Machine Learning

Sonnenburg, S., Braun ML, Ong, C., Bengio S, Bottou L, Holmes G, LeCun Y, Müller, K.-R., Pereira F, Rasmussen, C., Rätsch, G., Schölkopf, B., et al. (2007). The Need for Open Source Software in Machine Learning. Journal of Machine Learning Research, 8, 2443-2466. Retrieved from http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html.

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Sonnenburg, S1, Author           
Braun ML, Ong, CS2, Author           
Bengio S, Bottou L, Holmes G, LeCun Y, Müller, K-R2, Author           
Pereira F, Rasmussen, CE2, Author           
Rätsch, G2, Author           
Schölkopf, B2, Author           
Smola A, Vincent P, Weston, J2, Author           
Williamson, RC, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: Issued
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 Identifiers: URI: http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html
BibTex Citekey: 4768
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 2443 - 2466 Identifier: -