Dynamic logistic regression
An algorithm for training a logistic regression model on nonstationary classification problems based on maximising the evidence of updated predictions is described and illustrated on a number of synthetic problems.
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.