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The Need for Open Source Software in Machine Learning

Soren Sonnenburg
Mikio L. Braun
Cheng Soon Ong
Samy Bengio
Leon Bottou
Geoff Holmes
Yann LeCun
Klaus-Robert Mueller
Carl-Edward Rasmussen
Gunnar Raetsch
Bernhard Schoelkopf
Alexander Smola
Pascal Vincent
Jason Weston
Robert C. Williamson
Journal of Machine Learning Research, vol. 8 (2007), pp. 2443-2466


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 utilized, 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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