Newsgroups: comp.ai.jair.announce
Path: cantaloupe.srv.cs.cmu.edu!bb3.andrew.cmu.edu!newsfeed.pitt.edu!gatech!newsfeed.internetmci.com!in2.uu.net!news.reference.com!cnn.nas.nasa.gov!eos!kronos.arc.nasa.gov!jair-ed
From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Active Learning with Statistical ...
Message-ID: <1996Mar30.082259.674@ptolemy-ethernet.arc.nasa.gov>
Originator: jair-ed@polya.arc.nasa.gov
Lines: 40
Sender: usenet@ptolemy-ethernet.arc.nasa.gov (usenet@ptolemy.arc.nasa.gov)
Nntp-Posting-Host: polya.arc.nasa.gov
Organization: NASA/ARC Computational Sciences Division
Date: Sat, 30 Mar 1996 08:22:59 GMT
Approved: jair-ed@ptolemy.arc.nasa.gov

JAIR is pleased to announce the publication of the following article:

Cohn, D.A., Ghahramani, Z., and Jordan, M.I. (1996)
  "Active Learning with Statistical Models", 
   Volume 4, pages 129-145.

   Available in HTML, Postscript (325K) and compressed Postscript (121K).
   For quick access via your WWW browser, use this URL:
     http://www.cs.washington.edu/research/jair/abstracts/cohn96a.html
   More detailed instructions are below.

   Abstract: For many types of machine learning algorithms, one can
   compute the statistically `optimal' way to select training data.  In
   this paper, we review how optimal data selection techniques have been
   used with feedforward neural networks.  We then show how the same
   principles may be used to select data for two alternative,
   statistically-based learning architectures: mixtures of Gaussians and
   locally weighted regression.  While the techniques for neural networks
   are computationally expensive and approximate, the techniques for
   mixtures of Gaussians and locally weighted regression are both
   efficient and accurate.  Empirically, we observe that the optimality
   criterion sharply decreases the number of training examples the
   learner needs in order to achieve good performance.

The article is available via:
   
 -- comp.ai.jair.papers (also see comp.ai.jair.announce)

 -- World Wide Web: The URL for our World Wide Web server is
       http://www.cs.washington.edu/research/jair/home.html
    For direct access to this article and related files try:
       http://www.cs.washington.edu/research/jair/abstracts/cohn96a.html

 -- Anonymous FTP from either of the two sites below.

    Carnegie-Mellon University (USA):
	ftp://p.gp.cs.cmu.edu/usr/jair/pub/volume4/cohn96a.ps
    The University of Genoa (Italy):
	ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume4/cohn96a.ps

    The compressed PostScript file is named cohn96a.ps.Z (121K)

 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it
    with the subject AUTORESPOND and our automailer will respond. To
    get the Postscript file, use the message body GET volume4/cohn96a.ps 
    (Note: Your mailer might find this file too large to handle.) 
    Only one can file be requested in each message.

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

For more information about JAIR, visit 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.



