Tech Report Available in Neuroprose

[Mark Plutowski]

The following report has been placed in the neuroprose archives at 
Ohio State University:

		     UCSD CSE Technical Report No. CS91-180

		     Active selection of training examples 
		for network learning in noiseless environments.  

				Mark Plutowski 
	  Department of Computer Science and Engineering, UCSD,  and

				Halbert White
      Institute for Neural Computation and Department of Economics, UCSD.


				   Abstract:

	We derive a method for {\sl actively selecting} examples to be 
	used in estimating an unknown mapping with a multilayer feedforward 
	network architecture.  Active selection chooses from among a set of 
	available examples an example which, when added to the previous set 
	of training examples and learned, maximizes the decrement of network 
	error over the input space.  New examples are chosen according to 
	network performance on previous training examples.  In practice, this 
	amounts to incrementally growing the training set as necessary to 
	achieve the desired level of accuracy.

	The objective is to minimize the data requirement of learning. 
	Towards this end, we choose a general criterion for selecting training 
	examples that works well in conjunction with the criterion used for 
	learning, here, least squares.  Examples are chosen to minimize 
	Integrated Mean Square Error (IMSE). IMSE embodies the effects of bias
	(misspecification of the network model) and variance (sampling variation
	due to noise).  We consider a special case of IMSE, Integrated Squared 
	Bias, (ISB)  to derive a selection criterion ($\Delta ISB$) which we 
	maximize to select new training examples.  $\Delta ISB$ is applicable 
	whenever sampling variation due to noise can be ignored. We conclude 
	with graphical illustrations of the method, and demonstrate its use 
	during network training. 


-=-=-=-=-=-=-=-=-=-=-=-=-=-= How to obtain a copy -=-=-=-=-=-=-=-=-=-=-=-=-=-=

Copies may be obtained by 

a) FTP directly from the Neuroprose directory, or
b) by land mail from the CSE dept. at UCSE.


a) via FTP:

To obtain a copy from Neuroprose, either use the "getps" program, or
ftp the file as follows:

% ftp cheops.cis.ohio-state.edu
Connected to cheops.cis.ohio-state.edu.
220 cheops.cis.ohio-state.edu FTP server (Version 5.49 Tue May 9 14:01:04 EDT 1989) ready.
Name (cheops.cis.ohio-state.edu:your-ident): anonymous
[2331 Guest login ok, send ident as password.
Password: your-ident
230 Guest login ok, access restrictions apply.
ftp> cd pub/neuroprose
250 CWD command successful.
ftp> binary
200 Type set to I.
ftp> get plutowski.active.ps.Z
200 PORT command successful.
150 Opening BINARY mode data connection for plutowski.active.ps.Z (348443 bytes).
226 Transfer complete.
local: plutowski.active.ps.Z remote: plutowski.active.ps.Z
348443 bytes received in 44 seconds (7.2 Kbytes/s)
ftp> quit
% uncompress plutowski.active.ps.Z
% lpr -P<printer-name> plutowski.active.ps


b) via postal mail:

Requests for hardcopies may be sent to:

	Kay Hutcheson
	CSE Department, 0114
	UCSD
	La Jolla, CA 92093-0114

and enclose a check for $5.00 payable to "UC Regents."
The report number is:  Technical Report No. CS91-180