Title :
Class-specific feature sets in classification
Author :
Baggenstoss, Paul M.
Author_Institution :
Naval Underwater Syst. Center, Newport, RI, USA
fDate :
12/1/1999 12:00:00 AM
Abstract :
In this correspondence, we present a new approach to the design of probabilistic classifiers that circumvents the dimensionality problem. Rather than working with a common high-dimensional feature set, the classifier is written in terms of likelihood ratios with respect to a common class using sufficient statistics chosen specifically for each class
Keywords :
probability; signal classification; statistical analysis; Class-specific feature sets; dimensionality problem; likelihood ratios; probabilistic classifiers; signal classification; sufficient statistics; Bayesian methods; Signal analysis; State estimation; Statistics; Training data; White noise;
Journal_Title :
Signal Processing, IEEE Transactions on
