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.

Date Published: 18 September 1998
PDF: 11 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323871

Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)