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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) (KAR id:57317)

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Hidden Markov models (HMMs) are usually learned using

to local optima. Spectral learning for HMMs provides a unique, optimal

with access to limited data, there is no means of estimating the accuracy

method has been proposed which can be used to assess whether the

proposed method is designed for real-life datasets where the true model is

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: 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: 16 Feb 2021 13:37 UTC
Resource URI: (The current URI for this page, for reference purposes)
Grzes, Marek:
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