Piece-wise quadratic classifier for multichoice decision environments

Belur V. Dasarathy

doi:10.1117/12.323871

18 September 1998 Piece-wise quadratic classifier for multichoice decision environments

Belur V. Dasarathy

Proceedings Volume 3371, Automatic Target Recognition VIII; (1998) https://doi.org/10.1117/12.323871
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The concepts underlying two of the common classifier concepts used in multi-choice decision environments, namely the Bayes classifier and the piece-wise linear classifier, are combined in this study to define a piece-wise quadratic classifier. This results in decision surfaces that are complex combinations of the traditional quadratic surfaces defined by the Bayes classifier under the Gaussian assumptions, but would be applicable in environments wherein such Gaussian assumptions may not be truly valid. The paper describes the methodology in detail along with the specifics of the learning and classification algorithms. Experimental results based on standard data sets available in the literature and on the Internet, are included to illustrate the benefits and limitations of the methodology.

Citation Download Citation

Belur V. Dasarathy "Piece-wise quadratic classifier for multichoice decision environments", Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); https://doi.org/10.1117/12.323871

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