Dynamic logistic regression

Author

Penny, William D. ; Roberts, Stephen J.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

3

fYear

1999

fDate

1999

Firstpage

1562

Abstract

We propose an online learning algorithm for training a logistic regression model on nonstationary classification problems. The nonstationarity is captured by modelling the weights in a logistic regression classifier as evolving according to a first order Markov process. The weights are updated using the extended Kalman filter formalism and nonstationarities are tracked by inferring a time-varying state noise variance parameter. We describe an algorithm for doing this based on maximising the evidence of updated predictions. The algorithm is illustrated on a number of synthetic problems

Keywords

Bayes methods; Kalman filters; Markov processes; learning (artificial intelligence); nonlinear filters; parameter estimation; pattern classification; state estimation; statistical analysis; dynamic logistic regression; extended Kalman filter; first order Markov process; logistic regression classifier; nonstationarities; nonstationary classification problems; online learning algorithm; time-varying state noise variance parameter; updated predictions; Bayesian methods; Convergence; Councils; Educational institutions; Logistics; Markov processes; Prediction algorithms; Predictive models; State-space methods; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832603

Filename

832603