Prior-updating ensemble learning for discrete HMM

Ensemble learning is a variational Bayesian method in which an intractable distribution is approximated by a lower-bound. Ensemble learning results in models with better generalization and is less likely to fall into local maxima than Baum-Welch learning. However, it does not fully make use of the statistics of training data. In this paper, we propose a prior-updating variant which combines the data-driven property of Baum-Welch learning and the generalization property of ensemble learning. First we present experimental results suggesting that ensemble learning is better than the Baum-Welch learning in the aspects mentioned above and then we introduce a prior-updating method using training data. The prior-updating ensemble learning performs better than Baum-Welch as well as the pure ensemble learning in experiments with an artificial and a real data set.