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A stochastic memoizer for sequence data

Abstract:

We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares statistical strength between subsequent symbol predictive distributions in such a way that predictive performance generalizes well. The model builds on a specific parameterization of an unbounded-depth hierarchical Pitman-Yor process. We introduce analytic marginalization steps (using coagulation operators) to reduce this...

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Journal:
Proceedings of the 26th International Conference On Machine Learning, ICML 2009
Pages:
1129-1136
Publication date:
2009-01-01
URN:
uuid:d0e4822f-555b-49c0-995a-96a63293e48a
Source identifiers:
353230
Local pid:
pubs:353230
Language:
English

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