[comp.ai.neural-nets] New TR Available

whart@beowulf.ucsd.edu (Bill Hart) (02/20/91)

The following TR has been placed in the neuroprose archives at 
Ohio State University.

--Bill

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=


		     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 -=-=-=-=-=-=-=-=-=-=-=-=-=-=

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 (325222 bytes).
226 Transfer complete.
local: plutowski.active.ps.Z remote: plutowski.active.ps.Z
325222 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