Title :
Classification with a combined information test
Author :
Lynch, Robert ; Willett, Peter
Author_Institution :
Naval Undersea Warfare Center, New London, CT, USA
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.550522
