Classification of Gaussian data with sieve-regularized estimates

Natalia A. Schmid; Joseph A. O'Sullivan

doi:10.1117/12.445405

22 October 2001 Classification of Gaussian data with sieve-regularized estimates

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445405
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Natalia A. Schmid and Joseph A. O'Sullivan "Classification of Gaussian data with sieve-regularized estimates", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445405

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