Liza, Farhana Ferdousi, Grzes, Marek (2016) Estimating the Accuracy of Spectral Learning for HMMs. In: Estimating the Accuracy of Spectral Learning for HMMs. Lecture Notes in Computer Science . pp. 46-56. Springer ISBN 978-3-319-44747-6. E-ISBN 978-3-319-44748-3. (doi:10.1007/978-3-319-44748-3_5) (KAR id:57317)
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Official URL: http://dx.doi.org/10.1007/978-3-319-44748-3_5 |
Abstract
Hidden Markov models (HMMs) are usually learned using
the expectation maximisation algorithm which is, unfortunately, subject
to local optima. Spectral learning for HMMs provides a unique, optimal
solution subject to availability of a sufficient amount of data. However,
with access to limited data, there is no means of estimating the accuracy
of the solution of a given model. In this paper, a new spectral evaluation
method has been proposed which can be used to assess whether the
algorithm is converging to a stable solution on a given dataset. The
proposed method is designed for real-life datasets where the true model is
not available. A number of empirical experiments on synthetic as well as
real datasets indicate that our criterion is an accurate proxy to measure
quality of models learned using spectral learning.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.1007/978-3-319-44748-3_5 |
Uncontrolled keywords: | Spectral learning, HMM, SVD, Evaluation technique |
Subjects: |
Q Science > Q Science (General) > Q335 Artificial intelligence Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Marek Grzes |
Date Deposited: | 15 Sep 2016 21:07 UTC |
Last Modified: | 09 Dec 2022 01:37 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/57317 (The current URI for this page, for reference purposes) |
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