A fuzzy domain adaptation method based on self-constructing fuzzy neural network

Hao, P; Zhang, G; Behbood, V; Zheng, Z

A fuzzy domain adaptation method based on self-constructing fuzzy neural network

Hao, P Zhang, G

Behbood, V Zheng, Z

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Publication Type:: Conference Proceeding
Citation:: Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014, 2014, pp. 676 - 681
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Hao, P	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Behbood, V	en_US
dc.contributor.author	Zheng, Z	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014, 2014, pp. 676 - 681	en_US
dc.identifier.isbn	9789814619967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37417
dc.description.abstract	© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Domain adaptation addresses the problem of how to utilize a model trained in the source domain to make predictions for target domain when the distribution between two domains differs substantially and labeled data in target domain is costly to collect for retraining. Existed studies are incapable to handle the issue of information granularity, in this paper, we propose a new fuzzy domain adaptation method based on self-constructing fuzzy neural network. This approach models the transferred knowledge supporting the development of the current models granularly in the form of fuzzy sets and adapts the knowledge using fuzzy similarity measure to reduce prediction error in the target domain.	en_US
dc.relation.ispartof	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014	en_US
dc.title	A fuzzy domain adaptation method based on self-constructing fuzzy neural network	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brazil
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Domain adaptation addresses the problem of how to utilize a model trained in the source domain to make predictions for target domain when the distribution between two domains differs substantially and labeled data in target domain is costly to collect for retraining. Existed studies are incapable to handle the issue of information granularity, in this paper, we propose a new fuzzy domain adaptation method based on self-constructing fuzzy neural network. This approach models the transferred knowledge supporting the development of the current models granularly in the form of fuzzy sets and adapts the knowledge using fuzzy similarity measure to reduce prediction error in the target domain.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/37417