Tensor Decomposition-Based Training Method for High-Order Hidden Markov Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354899" target="_blank" >RIV/68407700:21230/21:00354899 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-2962/paper28.pdf" target="_blank" >http://ceur-ws.org/Vol-2962/paper28.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Tensor Decomposition-Based Training Method for High-Order Hidden Markov Models
Original language description
Hidden Markov models (HMMs) are one of the most widely used unsupervised-learning algorithms for modeling discrete sequential data. Traditionally, most of the applications of HMMs have utilized only models of order 1 because higher-order models are computationally hard to train. We reformulate HMMs using tensor decomposition to efficiently build higher-order models with the use of stochastic gradient descent. Based on this, we propose a new modified version of a training algorithm for HMMs, especially suitable for high-order HMMs. Further, we show its capabilities and convergence on synthetic data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021)
ISBN
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
7
Pages from-to
39-45
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Heľpa
Event date
Sep 24, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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