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 used, 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.

@article{sonnenburg-2007,
  author = {Sonnenburg, S\"{o}ren and Braun, Mikio L. and Ong, Cheng Soon and Bengio, Samy and Bottou, L\'{e}on and Holmes, Geoffrey and {LeCun}, Yann and M\"{u}ller, Klaus-Robert and Pereira, Fernando and Rasmussen, Carl Edward and R\"{a}tsch, Gunnar and Sch\"{o}lkopf, Bernhard and Smola, Alexander and Vincent, Pascal and Weston, Jason and Williamson, Robert},
  title = {The Need for Open Source Software in Machine Learning},
  journal = {Journal of Machine Learning Research},
  year = {2007},
  volume = {8},
  pages = {2443-2466},
  month = {October},
  url = {http://leon.bottou.org/papers/sonnenburg-2007},
}

This is a collective proposal for more open software in the machine learning research community. My minor contribution was to play the devil's advocate. See also the MLOSS web site.