Paper: Active Learning with Statistical Models

[David Cohn]

Anticipating the post-NIPS rush, I would like to announce that
the following paper is available by anonymous ftp and web-server as

	ftp://psyche.mit.edu/pub/cohn/active-models.ps.Z

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Active Learning with Statistical Models

David A. Cohn, Zoubin Ghahramani and Michael I. Jordan
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology

For many types of learners one can compute the statistically "optimal"
way to select data.  We review how these 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 expensive and approximate, the techniques for mixtures of
Gaussians and locally weighted regression are both efficient and
accurate.

To appear in G. Tesauro, D. Touretzky, and J. Alspector, eds.,
Advances in Neural Information Processing Systems 7. Morgan Kaufmann,
San Francisco, CA (1995).

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The paper may also be retrieved by anonymous ftp to "psyche.mit.edu"
using the following protocol:

unix> ftp psyche.mit.edu

Name (psyche.mit.edu:joebob): anonymous    <- use "anonymous" here
331 Guest login ok, send ident as password.
Password: joebob at machine.univ.edu	   <- use your email address here
230 Guest login ok, access restrictions apply.
ftp> cd pub/cohn                           <- go to the directory
250 CWD command successful.
ftp> binary                                <- change to binary transfer
200 Type set to I.
ftp> get active-models.ps.Z                 <- get the file
200 PORT command successful.
150 Binary data connection for active-models.ps.Z ...
226 Binary Transfer complete.
local: active-models.ps.Z remote: active-models.ps.Z
301099 bytes received in 2.8 seconds (1e+02 Kbytes/s)
ftp> quit                                  <- all done
221 Goodbye.