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comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
Section - Mailing lists, BBS, CD-ROM?

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Top Document: comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
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See reader questions & answers on this topic! - Help others by sharing your knowledge

   See also "Other NN links?" in Part 7 of the FAQ.

1. Machine Learning mailing list
++++++++++++++++++++++++++++++++

   http://groups.yahoo.com/group/machine-learning/ 

   The Machine Learning mailing list is an unmoderated mailing list intended
   for people in Computer Sciences, Statistics, Mathematics, and other areas
   or disciplines with interests in Machine Learning. Researchers,
   practitioners, and users of Machine Learning in academia, industry, and
   government are encouraged to join the list to discuss and exchange ideas
   regarding any aspect of Machine Learning, e.g., various learning
   algorithms, data pre-processing, variable selection mechanism, instance
   selection, and applications to real-world problems. 

   You can post, read, and reply messages on the Web. Or you can choose to
   receive messages as individual emails, daily summaries, daily full-text
   digest, or read them on the Web only. 

2. The Connectionists Mailing List 
+++++++++++++++++++++++++++++++++++

   http://www.cnbc.cmu.edu/other/connectionists.html 

   CONNECTIONISTS is a moderated mailing list for discussion of technical
   issues relating to neural computation, and dissemination of professional
   announcements such as calls for papers, book announcements, and
   electronic preprints. CONNECTIONISTS is focused on meeting the needs of
   active researchers in the field, not on answering questions from
   beginners. 

3. Central Neural System Electronic Bulletin Board
++++++++++++++++++++++++++++++++++++++++++++++++++

      URL: ftp://www.centralneuralsystem.com/pub/CNS/
      Supported by: Wesley R. Elsberry
                    3027 Macaulay Street
                    San Diego, CA 92106
      Email: welsberr@inia.cls.org
      Alternative URL: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/neural/cns/0.html
      

   Many MS-DOS PD and shareware simulations, source code, benchmarks,
   demonstration packages, information files; some Unix, Macintosh, Amiga
   related files. Also available are files on AI, AI Expert listings
   1986-1991, fuzzy logic, genetic algorithms, artificial life, evolutionary
   biology, and many Project Gutenberg and Wiretap e-texts. 

4. AI CD-ROM
++++++++++++

   Network Cybernetics Corporation produces the "AI CD-ROM". It is an
   ISO-9660 format CD-ROM and contains a large assortment of software
   related to artificial intelligence, artificial life, virtual reality, and
   other topics. Programs for OS/2, MS-DOS, Macintosh, UNIX, and other
   operating systems are included. Research papers, tutorials, and other
   text files are included in ASCII, RTF, and other universal formats. The
   files have been collected from AI bulletin boards, Internet archive
   sites, University computer deptartments, and other government and
   civilian AI research organizations. Network Cybernetics Corporation
   intends to release annual revisions to the AI CD-ROM to keep it up to
   date with current developments in the field. The AI CD-ROM includes
   collections of files that address many specific AI/AL topics including
   Neural Networks (Source code and executables for many different platforms
   including Unix, DOS, and Macintosh. ANN development tools, example
   networks, sample data, tutorials. A complete collection of Neural Digest
   is included as well.) The AI CD-ROM may be ordered directly by check,
   money order, bank draft, or credit card from: 

           Network Cybernetics Corporation;
           4201 Wingren Road Suite 202;
           Irving, TX 75062-2763;
           Tel 214/650-2002;
           Fax 214/650-1929;

   The cost is $129 per disc + shipping ($5/disc domestic or $10/disc
   foreign) (See the comp.ai FAQ for further details) 

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Top Document: comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
Previous Document: Neural Network Associations?
Next Document: How to benchmark learning methods?

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