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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

214

Číslo periodika v rámci svazku

2016

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

126-133

Kód UT WoS článku

000386741300014

EID výsledku v databázi Scopus

2-s2.0-84992530545