Optimal Experiment Design paper available

[David Cohn]

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       Neural Network Exploration Using Optimal Experiment Design
                 AI Lab Memo #1491/CBCL Paper #99
 
                          David A. Cohn
               Dept. of Brain and Cognitive Sciences
	       Massachusetts Institute of Technology
                       Cambridge, MA 02139

I consider the question "How should one act when the only goal is to
learn as much as possible?"  Building on the theoretical results of
Fedorov and MacKay, I apply techniques from Optimal Experiment Design
(OED) to guide the query/action selection of a neural network learner.
I demonstrate that these techniques allow the learner to minimize its
generalization error by exploring its domain efficiently and
completely.  I conclude that, while not a panacea, OED-based
query/action has much to offer, especially in domains where its high
computational costs can be tolerated.
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The above paper is a greatly expanded version of one that appeared at
last year's NIPS, and is available by anonymous ftp to:
             publications.ai.mit.edu

in the file: ai-publications/1994/AIM-1491.ps.Z

It is also available from my home page at:
             http://www.ai.mit.edu/people/cohn/cohn.html

I welcome all comments, questions, and (gentle) criticisms.

 -David Cohn				e-mail: cohn at psyche.mit.edu
  Dept. of Brain & Cognitive Science	phone:  (617) 253-8409
  MIT, E10-243
  Cambridge, MA 02139
  http://www.ai.mit.edu/people/cohn/cohn.html