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Proceedings Paper

Classification of Gaussian data with sieve-regularized estimates
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Paper Abstract

Many classification problems use image or other high-dimensional data, and must be designed from training data. The design and analysis of such systems parameterized by unknown functions, based on a method of sieves to regularize the function estimates, is described. The test statistic is assumed to be the ideal test statistic with estimated functions substituted for the truth. The test statistic is decomposed into approximation error and estimation error components, providing analytical tools for determining the optimal sieve size.

Paper Details

Date Published: 22 October 2001
PDF: 10 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445405
Show Author Affiliations
Natalia A. Schmid, Univ. of Illinois/Urbana-Champaign (United States)
Joseph A. O'Sullivan, Washington Univ. (United States)


Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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