A Markov chain approach to multiple classifier fusion

Authors:
S. P. Luttrell
Affiliations:
QinetiQ, Malvern, Worcestershire, UK
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

Citing 2
Cited 0

A theory of self-organising neural networks

MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Stochastic Complexity in Statistical Inquiry Theory

Stochastic Complexity in Statistical Inquiry Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of this paper is to show how the problem of optimising the processing chain in a classifier combiner network can be recast as a Markov chain optimisation problem. A summary of the application of coding theory to Markov chains and to encoder networks is presented, and the key idea of optimising the joint probability density of the state of the processing chain is stated. An example of the application of these ideas to processing data from multiple correlated sources (i.e. a hierarchically correlated phase screen) is then given.