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Liza, Farhana Ferdousi 
ORCID: https://orcid.org/0000-0003-4854-5619 and Grześ, Marek
(2016)
Estimating the accuracy of spectral learning for HMMs.
In:
Artificial Intelligence.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
.
Springer-Verlag Berlin Heidelberg, BGR, pp. 46-56.
ISBN 9783319447476
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: | Book Section |
|---|---|
| Additional Information: | Publisher Copyright: © Springer International Publishing Switzerland 2016. |
| Uncontrolled Keywords: | evaluation technique,hmm,spectral learning,svd,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Sep 2024 16:30 |
| Last Modified: | 29 Sep 2024 06:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96820 |
| DOI: | 10.1007/978-3-319-44748-3_5 |
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