On Mon, 25 Feb 2008, Rudi Cilibrasi wrote: > Well said. I have been suggesting this for years. I believe the > scientific method is generally thought to include "verifiability" and > particularly "reproducibility" as criteria for a well-performed > experiment.
Let me comment on with 1 reference which imho should be mentioned to anyone doing ML (and not only) related research http://mloss.org/ (p.s. someone can treat it btw as the rich source of ideas for Debian packaging projects, there are 65 of them registered as of now and I am not sure what % is in Debian already and what % is worth being in Debian). and a paper that website references The Need for Open Source Software in Machine Learning Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert Williamson; 8, 2007 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 http://www.jmlr.org/papers/volume8/sonnenburg07a/sonnenburg07a.pdf -- Yaroslav Halchenko Research Assistant, Psychology Department, Rutgers-Newark Student Ph.D. @ CS Dept. NJIT Office: (973) 353-5440x263 | FWD: 82823 | Fax: (973) 353-1171 101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102 WWW: http://www.linkedin.com/in/yarik -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of "unsubscribe". Trouble? Contact [EMAIL PROTECTED]
