Title :
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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.832603
