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Estimating the Accuracy of Spectral Learning for HMMs

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)

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)
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: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Marek Grzes
Date Deposited: 15 Sep 2016 21:07 UTC
Last Modified: 29 May 2019 17:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57317 (The current URI for this page, for reference purposes)
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