Accession Number:

AD0720837

Title:

Pattern Recognition with Continous Parameter, Observable Markov Chains.

Descriptive Note:

Interim scientific rept. no. 10,

Corporate Author:

MICHIGAN STATE UNIV EAST LANSING DIV OF ENGINEERING RESEARCH

Personal Author(s):

Report Date:

1970-11-25

Pagination or Media Count:

34.0

Abstract:

The paper develops Bayesian learning and decision-making algorithms for the following pattern recognition problem. Each of M pattern classes is described by a continuous-parameter, discrete-state Markov chain having a finite number of states. All states and times of transition between states can be observed perfectly. The transition rate matrices, which establish the properties of the chains, are not known a priori. A Bayesian learning algorithm using a fixed amount of memory digests the training patterns which consist of a member function from each chain. This leads to an iterative, computationally simple, decision-making algorithm for classifying any portion of a member function. The Bhattacharyya bound and the probability of error are derived for the 2-state, 2-chain problem when the transition rate matrices are known. The last section reports on a computer simulation of a 3-state, 2-chain problem with varying amounts of training data. An appendix summerizes the pertinent facts about Markov chains. Author

Subject Categories:

  • Operations Research
  • Cybernetics
  • Bionics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE