S. Thrun, J. Langford, and D. Fox
Monte
Carlo Hidden Markov Models: Learning Non-Parametric Models of
Partially Observable Stochastic Processes
Proc. of the 16th International Conference on Machine Learning (ICML'99)
Abstract
We present a learning algorithm for non-parametric
hidden Markov models with continuous state and observation spaces. All
necessary probability densities are approximated using samples, along
with density trees generated from such samples. A Monte Carlo
version of Baum-Welch (EM) is employed to learn models from
data. Regularization during learning is achieved using an exponential
shrinking technique. The shrinkage factor, which determines the
effective capacity of the learning algorithm, is annealed down over
multiple iterations of Baum-Welch, and early stopping is applied to
select the right model. Once trained, Monte Carlo HMMs can be
run in an any-time fashion. We prove that under mild assumptions,
Monte Carlo Hidden Markov Models converge to a local
maximum in likelihood space, just like conventional HMMs. In addition,
we provide empirical results obtained in a gesture recognition
domain.
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Bibtex
@INPROCEEDINGS{Thr99Mon,
AUTHOR
= {Thrun, S. and Langford, J. and Fox, D.},
TITLE
= {Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes},
YEAR
= {1999},
BOOKTITLE = {Proc.~of the International Conference on Machine Learning}
}
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