Classification with a combined information test

Author

Lynch, Robert ; Willett, Peter

Author_Institution

Naval Undersea Warfare Center, New London, CT, USA

Volume

6

fYear

1996

fDate

7-10 May 1996

Firstpage

3061

Abstract

We introduce a discrete model for classifying a target that combines the information in training and test data to infer about the true symbol probabilities. Two tests are derived given that the symbols are distributed as a multinomial. The robustness of these tests lies in their ability to effectively use all of the information in the training and test data before making a classification decision. This is demonstrated by comparing their performance to a standard hypothesis test for a classification problem involving transmission of quantized data to a fusion center

Keywords

feature extraction; information theory; probability; quantisation (signal); sensor fusion; signal processing; GLRT; combined information test; discrete model; fusion center; generalized likelihood ratio test; multinomial distribution; performance; quantized data transmission; standard hypothesis test; symbol probabilities; target classification; test data; test robustness; training data; Maximum likelihood estimation; Robustness; Statistical analysis; Statistical distributions; Terminology; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.550522

Filename

550522