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ICML
2005
IEEE

Active learning for Hidden Markov Models: objective functions and algorithms

9 years 11 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
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.
Brigham Anderson, Andrew Moore
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Brigham Anderson, Andrew Moore
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