16.txt

This complete matlab for neural network

发信人: yaomc (白头翁&山东大汉), 信区: DataMining
标  题: [合集]传统期刊的没落--Machine Learning编委集体辞职
发信站: 南京大学小百合站 (Fri Nov 30 16:16:40 2001), 站内信件

roamingo (漫步鸥) 于Wed Oct 17 15:47:33 2001提到：

10月8日, 国际知名AI刊物Machine Learning 40名编委集体辞职, 转而支持新近创刊
的Journal of Machine Learning Research. 后者的全部论文均可以在期刊的主页上
免费下载, 且论文的版权由作者保留, 这意味着作者即使在论文公开发表后, 仍可将其
论文放在个人网站上供他人下载, 而这一点对传统期刊来说是一种侵权行为. 作为读者
要访问传统期刊的论文全文必须交纳昂贵的注册费用. 上述转变反映了互联网时代传统
期刊运作方式的弊端和学术界渴望加强科研成果传播速度的希望.                   

英文: (http://www.kdnuggets.com/news/2001/n21/4i.html)

 From: Michael Jordan 
 Date: Mon, 8 Oct 2001 14:33:25 -0700 (PDT) 
 Subject: letter of resignation from Machine Learning journal 

 The forty people whose names appear below have resigned from the
 Editorial Board of the Machine Learning Journal (MLJ).  We would
 like to make our resignations public, to explain the rationale for
 our action, and to indicate some of the implications that we see for
 members of the machine learning community worldwide.

 The machine learning community has come of age during a period
 of enormous change in the way that research publications are
 circulated.  Fifteen years ago research papers did not circulate
 easily, and as with other research communities we were fortunate
 that a viable commercial publishing model was in place so that
 the fledgling MLJ could begin to circulate.  The needs of the
 community, principally those of seeing our published papers circulate
 as widely and rapidly as possible, and the business model of
 commercial publishers were in harmony.

 Times have changed.  Articles now circulate easily via the Internet,
 but unfortunately MLJ publications are under restricted access.
 Universities and research centers can pay a yearly fee of $1050 US to
 obtain unrestricted access to MLJ articles (and individuals can pay
 $120 US).  While these fees provide access for institutions and
 individuals who can afford them, we feel that they also have the
 effect of limiting contact between the current machine learning
 community and the potentially much larger community of researchers
 worldwide whose participation in our field should be the fruit of
 the modern Internet.

 None of the revenue stream from the journal makes its way back to
 authors, and in this context authors should expect a particularly
 favorable return on their intellectual contribution---they should
 expect a service that maximizes the distribution of their work.
 We see little benefit accruing to our community from a mechanism
 that ensures revenue for a third party by restricting the communication
 channel between authors and readers.

 In the spring of 2000, a new journal, the Journal of Machine Learning
 Research (JMLR), was created, based on a new vision of the journal
 publication process in which the editorial board and authors retain
 significant control over the journal's content and distribution.
 Articles published in JMLR are available freely, without limits and
 without conditions, at the journal's website, http://www.jmlr.org.
 The content and format of the website are entirely controlled by the
 editorial board, which also serves its traditional function of
 ensuring rigorous peer review of journal articles.  Finally, the
 journal is also published in a hardcopy version by MIT Press.

 Authors retain the copyright for the articles that they publish in
 JMLR.  The following paragraph is taken from the agreement that every
 author signs with JMLR (see www.jmlr.org/forms/agreement.pdf):

   You [the author] retain copyright to your article, subject only
   to the specific rights given to MIT Press and to the Sponsor [the
   editorial board] in the following paragraphs.  By retaining your
   copyright, you are reserving for yourself among other things unlimited
   rights of electronic distribution, and the right to license your work
   to other publishers, once the article has been published in JMLR
   by MIT Press and the Sponsor [the editorial board].  After first
   publication, your only obligation is to ensure that appropriate
   first publication credit is given to JMLR and MIT Press.

 We think that many will agree that this is an agreement that is
 reflective of the modern Internet, and is appealing in its recognition
 of the rights of authors to distribute their work as widely as possible.
 In particular, authors can leave copies of their JMLR articles on their
 own homepage.

 Over the years the editorial board of MLJ has expanded to encompass
 all of the various perspectives on the machine learning field, and
 the editorial board's efforts in this regard have contributed greatly
 to the sense of intellectual unity and community that many of us feel.
 We believe, however, that there is much more to achieve, and that
 our further growth and further impact will be enormously enhanced
 if via our flagship journal we are able to communicate more freely,
 easily, and universally.

 Our action is not unprecedented.  As documented at the Scholarly Publishing
 and Academic Resources Coalition (SPARC) website, http://www.arl.org/sparc,
 there are many areas in science where researchers are moving to low-cost
 publication alternatives.  One salient example is the case of the
 journal "Logic Programming".  In 1999, the editors and editorial
 advisors of this journal resigned to join "Theory and Practice of
 Logic Programming", a Cambridge University Press journal that encourages
 electronic dissemination of papers.

 In summary, our resignation from the editorial board of MLJ reflects
 our belief that journals should principally serve the needs of the
 intellectual community, in particular by providing the immediate and
 universal access to journal articles that modern technology supports,
 and doing so at a cost that excludes no one.  We are excited about JMLR,
 which provides this access and does so unconditionally.  We feel that
 JMLR provides an ideal vehicle to support the near-term and long-term
 evolution of the field of machine learning and to serve as the flagship
 journal for the field.  We invite all of the members of the community
 to submit their articles to the journal and to contribute actively to
 its growth.

 Sincerely yours,

   Chris Atkeson
   Peter Bartlett
   Andrew Barto
   Jonathan Baxter
   Yoshua Bengio
   Kristin Bennett
   Chris Bishop
   Justin Boyan
   Carla Brodley
   Claire Cardie
   William Cohen
   Peter Dayan
   Tom Dietterich
   Jerome Friedman
   Nir Friedman
   Zoubin Ghahramani
   David Heckerman
   Geoffrey Hinton
   Haym Hirsh
   Tommi Jaakkola
   Michael Jordan
   Leslie Kaelbling
   Daphne Koller
   John Lafferty
   Sridhar Mahadevan
   Marina Meila
   Andrew McCallum
   Tom Mitchell
   Stuart Russell
   Lawrence Saul
   Bernhard Schoelkopf
   John Shawe-Taylor
   Yoram Singer
   Satinder Singh
   Padhraic Smyth
   Richard Sutton
   Sebastian Thrun
   Manfred Warmuth
   Chris Williams
   Robert Williamson


yaomc (白头翁&山东大汉) 于Wed Oct 17 16:03:43 2001)
提到：

以前就听说，学术界急需要将所有的科研成果公开的消息，目的就是为了避免不必要的重
复劳动和少走弯路，以节省本就不富裕的资金和精力，不过，因为这牵涉到更多出版商的
利益。所以，这次的大辞职或许能够加速这一要求得实现。
这对于发展中国家来说是比发达国家更好的消息，毕竟实力相差得太大了，投入的资金也
是无法比拟的。我们期待着更多的类似的情况出现。




BombTokyo (轰炸东京) 于Wed Oct 17 20:13:32 2001)
提到：

同感，不然我们连论文都看不到，想要做点事情真是寸步难行。


yaomc (白头翁&山东大汉) 于Wed Oct 17 20:25:03 2001提到：

这种情况在中国应该是更严重的，看看现在中国发篇文章的版面费就知道了，
当然其中也掺杂了太多的其他因素，比如说职称、工资等等，前一段时间
万方数据还是免费的，现在好像也已经在收费了。我觉得如果在国外盛行了
这么一股风气，中国也肯定肯定跟着人家的屁股后边跑。