The Need for Open Source Software in Machine Learning
Venue
Journal of Machine Learning Research, vol. 8 (2007), pp. 2443-2466
Publication Year
2007
Authors
Soren Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoff Holmes, Yann LeCun, Klaus-Robert Mueller, Fernando Pereira, Carl-Edward Rasmussen, Gunnar Raetsch, Bernhard Schoelkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert C. Williamson
BibTeX
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 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.
