Adaptive online learning of generative stochastic models
J. C. Stiller · Complexity · 2003
Abstract In online learning problems to choice of the learning rate, the weight given to new observations with respect to the knowledge gained from old ones, is a difficult problem. A novel algorithm for the adaptive, i.e., data‐dependent choice of the learning rate is presented. It is based on the idea of maximum likelihood estimation. In contrast to other approaches it does not rely on the stochastic independence of the observed data, the stationarity of the data source or a priori information about parameter changes. This makes it well suited for the analysis of ongoing nonstationary dynamic processes. It can be applied to the online learning of various different generative stochastic models. We present the adaptive online learning of Hidden Markov Models. © 2003 Wiley Periodicals, Inc.