Optimal Bayesian classification in nonstationary streaming environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages609-616
Number of pages8
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period7/6/147/11/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Optimal Bayesian classification in nonstationary streaming environments'. Together they form a unique fingerprint.

Cite this