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Proceedings Paper

A computational approach for statistical learning and inference
Author(s): Xinjia Chen
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Paper Abstract

In this paper, we demonstrate that a wide class of machine learning problems can be formulated as general problems of multi-valued decision and classification. To reduce the sample complexity associated with the statistical learning and inference schemes, we propose the principle of probabilistic comparison, the inclusion principle and exact computational methods for constructing multistage procedures for the relevant multi-hypothesis testing problems.

Paper Details

Date Published: 10 May 2012
PDF: 12 pages
Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010T (10 May 2012); doi: 10.1117/12.918919
Show Author Affiliations
Xinjia Chen, Southern Univ. (United States)


Published in SPIE Proceedings Vol. 8401:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
Harold Szu, Editor(s)

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