Constructing optimal classifiers from sub-optimal composite hypothesis tests

Alan Schaum

doi:10.1117/12.2556105

14 May 2020 Constructing optimal classifiers from sub-optimal composite hypothesis tests

Alan Schaum

Author Affiliations +

Proceedings Volume 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI; 113920W (2020) https://doi.org/10.1117/12.2556105
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The general form of admissible solutions to classification problems depends on parameters π_i whose values determine performance. However, translating performance requirements into parameter choices requires a difficult evaluation of interdependent probabilities. In this report we build optimal classifiers by combining composite hypothesis tests. The process relates the parameters π_i to detection thresholds λ _jk , which are more directly predictive of detection and false alarm probabilities. It is found that the constituent composite hypothesis tests cannot be optimal, but instead must be constructed via clairvoyant fusion principles.

Citation Download Citation

Alan Schaum "Constructing optimal classifiers from sub-optimal composite hypothesis tests", Proc. SPIE 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, 113920W (14 May 2020); https://doi.org/10.1117/12.2556105

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