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From: minton@kronos.arc.nasa.gov (Steve Minton)
Subject: Re: Electronic neural net journals?
Message-ID: <1995Jan23.212511.23983@ptolemy-ethernet.arc.nasa.gov>
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Date: Mon, 23 Jan 1995 21:25:11 GMT
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In a recent message, Scott Fahlman mentioned JAIR, the Journal of
Artificial Intelligence Research, which is published both
electronically and in print. (Thanks Scott!) Articles are available
for no charge over the internet.  JAIR does invite submissions on
connectionism and neural networks, and I've appended an announcement
regarding a very recent article by Dietterich and Bakiri that readers
of this group may find of interest. The announcement includes
instructions for obtaining the article (and general information on
JAIR), and our URLs.

- Steve Minton (minton@ptolemy.arc.nasa.gov)
  JAIR Executive Editor

----------------------

Dietterich, T.G. and Bakiri, G. (1995)
  "Solving Multiclass Learning Problems via Error-Correcting Output Codes",
   Volume 2, pages 263-286.
   PostScript: volume2/dietterich95a.ps (265K)
  
   Abstract: Multiclass learning problems involve finding a definition
   for an unknown function f(x) whose range is a discrete set containing
   k>2 values (i.e., k ``classes'').  The definition is acquired by
   studying collections of training examples of the form <x_i, f(x_i)>.
   Existing approaches to multiclass learning problems include direct
   application of multiclass algorithms such as the decision-tree
   algorithms C4.5 and CART, application of binary concept learning
   algorithms to learn individual binary functions for each of the k
   classes, and application of binary concept learning algorithms with
   distributed output representations.  This paper compares these three
   approaches to a new technique in which error-correcting codes are
   employed as a distributed output representation.  We show that these
   output representations improve the generalization performance of both
   C4.5 and backpropagation on a wide range of multiclass learning tasks.
   We also demonstrate that this approach is robust with respect to
   changes in the size of the training sample, the assignment of
   distributed representations to particular classes, and the application
   of overfitting avoidance techniques such as decision-tree pruning.
   Finally, we show that---like the other methods---the error-correcting
   code technique can provide reliable class probability estimates.
   Taken together, these results demonstrate that error-correcting output
   codes provide a general-purpose method for improving the performance
   of inductive learning programs on multiclass problems.

The PostScript file is available via:
   
 -- comp.ai.jair.papers

 -- World Wide Web: The URL for our World Wide Web server is
       http://www.cs.washington.edu/research/jair/home.html

 -- Anonymous FTP from either of the two sites below:
      CMU:   p.gp.cs.cmu.edu        directory: /usr/jair/pub/volume2
      Genoa: ftp.mrg.dist.unige.it  directory:  pub/jair/pub/volume2

 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it
    with the subject AUTORESPOND, and the body GET VOLUME2/DIETTERICH95A.PS
    (either upper or lowercase is fine). 
    Note: Your mailer might find this file too large to handle.
          (The compressed version of this paper cannot be mailed.)

 -- JAIR Gopher server: At p.gp.cs.cmu.edu, port 70. 

For more information about JAIR, check out our WWW or FTP sites, or
send electronic mail to jair@cs.cmu.edu with the subject AUTORESPOND
and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov.


