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The Need for Open Source Software in Machine Learning

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84960

Sonnenburg,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84118

Braun ML, Ong,  CS
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Bengio S, Bottou L, Holmes G, LeCun Y, Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Pereira F, Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Smola A, Vincent P, Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CB87-C
Zusammenfassung
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.