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Proceedings Paper

Realistic world of limited sample neural network applications: how to proceed on a firm methodological foundation with small-n
Author(s): Gary M. Jackson
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Paper Abstract

Improving evaluation, especially with small sample, or small-n, applications, may be highly dependent on incorporating expanding knowledge about methodological pitfalls to avoid. It is the intent of the current paper to provide an informational guide to key evaluation issues with small-n. Although the present paper is focused on supervised learning classification paradigms typified by the back-propagation network, the principles hold true in various degrees for other artificial neural networks.

Paper Details

Date Published: 1 February 1994
PDF: 10 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172507
Show Author Affiliations
Gary M. Jackson, Consultant to U.S. Government (United States)

Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike M.D.; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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