Machine Learning Online

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From: "Mike Groth" <mgroth@wkap.com>
Subject: Machine Learning Online

     http://mlis.www.wkap.nl
     (Link inactive 15 June 2004)

     http://www.kluweronline.com/issn/0885-6125
     (Link active 15 June 2004)
     
     Machine Learning Online
     
     Editor-in-Chief:
     Thomas G. Dietterich, Oregon State University and Arris 
     Pharmaceutical, Inc.
     
     Machine Learning Online is a new online version of Kluwer's highly 
     successful Machine Learning Journal.  Although much of the service 
     is free, the full authorative text of the journal articles is 
     accessible only to subscribers.
     
     The journal, Machine Learning, is an international forum for research 
     on computational approaches to learning. The journal publishes 
     articles reporting substantive research results on a wide range 
     of learning methods applied to a variety of task domains, including 
     but not limited to: 
     
     Methods: Inductive learning methods; Explanation-based learning; 
     Genetic algorithms; Analogy and case-based methods; Connectionist 
     techniques; Automated knowledge acquisition; Learning from instruction.
     
     Task Domains: Classification and recognition; Problem solving and 
     planning; Reasoning and inference; Natural language processing; 
     Design and diagnosis; Vision and speech perception; Robotics and motor 
     control. 
     
     At the Machine Learning Online web site, the true ease and power of 
     electronic publishing can be experienced in the multiple indices, the 
     hypertext links between articles and the fast, powerful form-based 
     search facility which produces a list of documents and highlights the 
     found words in the paper.
     
     ML-Online also contains a complete biographical reference of published 
     papers, a full set of appendices and a facility which enables the 
     reader to send a letter directly to the editor-in-chief with a single 
     click of the mouse button.  As a part of the free component, Kluwer is 
     offering a much-needed service: a reliable doorway to other information 
     on the Internet which relates to Machine Learning.  A newly appointed, 
     online editor ensures that these links are kept up-to-date, accurate 
     and comprehensive.
     
     Kluwer is confident that ML-Online is a major step forward, making the 
     information more accessible to more people, more quickly and more 
     flexibly.  It's powerful, it's fast and it's easy to use.
     
     Kluwer Academic Publishers         TEL: (617) 871-6600 
     101 Philip Drive                   FAX: (617) 871-6528
     Norwell, MA  02061                 E-mail: kluwer@wkap.com 
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