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Active learning for Hidden Markov Models: objective functions and algorithms

Published: 07 August 2005 Publication History

Abstract

Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by closing the data-gathering loop, i.e., they choose the examples most informative with respect to their learning objectives. We introduce a framework and objective functions for active learning in three fundamental HMM problems: model learning, state estimation, and path estimation. In addition, we describe a new set of algorithms for efficiently finding optimal greedy queries using these objective functions. The algorithms are fast, i.e., linear in the number of time steps to select the optimal query and we present empirical results showing that these algorithms can significantly reduce the need for labelled training data.

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cover image ACM Other conferences
ICML '05: Proceedings of the 22nd international conference on Machine learning
August 2005
1113 pages
ISBN:1595931805
DOI:10.1145/1102351
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 07 August 2005

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