Granular transfer learning using type-2 fuzzy HMM for text sequence recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099382" target="_blank" >RIV/61989100:27240/16:86099382 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231216305434" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231216305434</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2016.05.077" target="_blank" >10.1016/j.neucom.2016.05.077</a>

Result language
angličtina
Original language name
Granular transfer learning using type-2 fuzzy HMM for text sequence recognition
Original language description
Context information plays an important role in text sequence recognition, but it is difficult to harness the uncertainty caused by conflicting implications. In this paper, we propose a novel Granular Transfer (GT) learning with type-2 fuzzy Hidden Markov Model (HMM) called GT2HMM, in which interpretable granules' representation is introduced to describe the contextual uncertainty for its transfer learning. The correspondences among words are transformed into information granules using fuzzy c-means. To fulfill the utilization of granular information in sequence recognition, we construct a type-2 fuzzy HMM which fuses labeled data and unlabeled observations. With a tunable granularity, correspondence information is refined in a coarse-to-fine manner in GT2HMM. Experiments on transductive and inductive transfer learning in part-of-speech (POS) tagging tasks verify the effectiveness of our proposed GT2HMM. (C) 2016 Elsevier B.V.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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