A computational approach for statistical learning and inference

Xinjia Chen

doi:10.1117/12.918919

10 May 2012 A computational approach for statistical learning and inference

Xinjia Chen

Proceedings Volume 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X; 84010T (2012) https://doi.org/10.1117/12.918919
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Xinjia Chen "A computational approach for statistical learning and inference", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010T (10 May 2012); https://doi.org/10.1117/12.918919

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available