Markov sequential pattern recognition : dependency and the unknown class.

Malone, Kevin Thomas ; Haschke, Greg Benjamin ; Koch, Mark William

Sandia National Laboratories

关键词: Markov Processes.;    Pattern Recognition;    Markov Process;    Acoustic Models.;    Acoustics;   

DOI  :  10.2172/919134
RP-ID  :  SAND2004-4385
RP-ID  :  AC04-94AL85000
RP-ID  :  919134

美国|英语

来源: UNT Digital Library

【 摘 要 】

The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.