Liza, Farhana Ferdousi 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

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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,general computer science ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	26 Sep 2024 16:30
Last Modified:	16 Jun 2026 20:40
URI:	https://uea-test-2026.eprints-hosting.org/id/eprint/96820
DOI:	10.1007/978-3-319-44748-3_5

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