Training NN's with missing or noisy data

[ahmad@interval.com]

The following paper is available for anonymous ftp on
archive.cis.ohio-state.edu (128.146.8.52), in directory
pub/neuroprose, as file "tresp.deficient.ps.Z". (The companion paper,
"Some Solutions to the Missing Feature Problem in Vision" is available
as "ahmad.missing.ps.Z")

	     Training Neural Networks with Deficient Data

  Volker Tresp			Subutai Ahmad
  Siemens AG			Interval Research Corporation
  Central Research		1801-C Page Mill Rd.
  81730 Muenchen, Germany	Palo Alto, CA 94304
  tresp at zfe.siemens.de 		ahmad at interval.com

  		   Ralph Neuneier
  		   Siemens AG
  		   Central Research
  		   Otto-Hahn-Ring 6
  		   81730 Muenchen, Germany
  		   ralph at zfe.siemens.de 

Abstract:

We analyze how data with uncertain or missing input features can be
incorporated into the training of a neural network.  The general
solution requires a weighted integration over the unknown or uncertain
input although computationally cheaper closed-form solutions can be
found for certain Gaussian Basis Function (GBF) networks.  We also
discuss cases in which heuristical solutions such as substituting the
mean of an unknown input can be harmful.


The paper will appear in: 

Cowan, J.D., Tesauro, G., and Alspector, J.  (Eds.), Advances in
Neural Information Processing Systems 6. San Francisco CA: Morgan
Kaufmann, 1994.


Subutai Ahmad
Interval Research Corporation		       Phone: 415-354-3639
1801-C Page Mill Rd.				 Fax: 415-354-0872
Palo Alto, CA 94304			E-mail: ahmad at interval.com