JMLR: Workshop and Conference Proceedings

The Journal of Machine Learning Research (JMLR) is one of the leading journals in Machine Learning. Ranked the 7th in "Computer Science, Artificial Intelligence" category from the JCR, its impact factor is 2.682.

Beyond the quality of the journal and the papers published there, JMLR has represented a great initiative as the first quality Open Access journal in the Machine Learning field. From two years ago until now, JMLR tries to innovate with new initiatives like the support to the development of Open Source Machine Learning software or the recent creation of a special "Conference and Workshop Proceedings" series that aims publishing the work presented at Machine Learning Workshops and Conferences in an Open Access manner. These series have a ISSN (1938-7228) and is described by JMLR as follows

The JMLR: Workshop and Conference Proceedings series is a new series aimed specifically at publishing work presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference and will be pulished online on the JMLR web site. Authors will retain copyright and individual volume editors are free to make additional hardcopy publishing arrangments, but JMLR will not produce hardcopies of these volumes.

Computational Linguistics (CL) goes Open Access

Hal announces that CL journal would be open access from the first issue of the next year. There will be no print version of the journal and the electronic version will be Open Access.

The existence of an importan Open Access journal related to Computational Linguistics has been a discussion topic last years. On May 2007, Hal published the post "Whence JCLR?" where he discussed about the existence of the JMLR Journal, an Open Access Machine Learning journal that is one of the key journals for the ML community.

It is really a very good new for the CL community.

Journal of Interesting Negative Results in Natural Language Processing and Machine Learning

Johannes Fuernkranz sent this announcement to the ML-news list. I think it is a great new to the NLP and ML communities as some negative results can be even more useful than some positive results. This is a good way to prevent others to do not expend time exploring hypothesis that have been invalidated by others.

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Journal of Intersting Negative Results
http://www.jinr.org
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We are happy to announce the on-line publication of the first article in the Journal of Interesting Negative Results in Natural Language Processing and Machine Learning. Please visit http://www.jinr.org and click on "articles".

JINR is an electronic journal, with a printed version to be negotiated with a major publisher once we have established a steady presence. The journal will bring to the fore research in Natural Language Processing and Machine Learning that uncovers interesting negative results.

It is becoming more and more obvious that the research community in general, and those who work NLP and ML in particular, are biased towards publishing successful ideas and experiments. Insofar as both our research areas focus on theories "proven" via empirical methods, we are sure to encounter ideas that fail at the experimental stage for unexpected, and often interesting, reasons. Much can be learned by analysing why some ideas, while intuitive and plausible, do not work. The importance of counter-examples for disproving conjectures is already well known. Negative results may point to interesting and important open problems. Knowing directions that lead to dead-ends in research can help others avoid replicating paths that take them nowhere. This might accelerate progress or even break through walls!

We propose this journal as a resource that gives a voice to negative results which stem from intuitive and justifiable ideas, proven wrong through thorough and well-conducted experiments. We also encourage the submission of short papers/communications presenting counter-examples to usually accepted conjectures or to published papers.

The journal's scope encompasses all areas of Natural Language Processing and Machine Learning. Papers published in JINR will meet the highest quality standards, as measured by the originality and significance of the contribution. They will describe research with theoretical and practical significance. All theories and ideas will have to be clearly stated and justified by a deep literature review.