Efficient multitemplate learning for structured prediction

Mao, Q; Wai-Hung, I

Efficient multitemplate learning for structured prediction

Mao, Q Wai-Hung, I

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2013, 24 (2), pp. 248 - 261
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	2013004103OK.pdf		610.71 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Mao, Q	en_US
dc.contributor.author	Wai-Hung, I https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2013, 24 (2), pp. 248 - 261	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29709
dc.description.abstract	Conditional random fields (CRF) and structural support vector machines (structural SVM) are two state-of-theart methods for structured prediction that captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on all input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with an exponential number of constraints. Then we introduce an efficient cutting-plane algorithm to solve this problem in the primal and present its convergence. We also evaluate the proposed learning paradigm on two widely studied structured prediction tasks, i.e., sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and structural SVMs because of exploiting the importance of each template. Complexity analysis and empirical results also show that the proposed method is more efficient than Online multikernel learning on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized p-block norm regularization with p > 1, and experiments show competitive performances when p (1, 2).© 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2012.2228228	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Efficient multitemplate learning for structured prediction	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	24	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

Conditional random fields (CRF) and structural support vector machines (structural SVM) are two state-of-theart methods for structured prediction that captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on all input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with an exponential number of constraints. Then we introduce an efficient cutting-plane algorithm to solve this problem in the primal and present its convergence. We also evaluate the proposed learning paradigm on two widely studied structured prediction tasks, i.e., sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and structural SVMs because of exploiting the importance of each template. Complexity analysis and empirical results also show that the proposed method is more efficient than Online multikernel learning on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized p-block norm regularization with p > 1, and experiments show competitive performances when p (1, 2).© 2012 IEEE.

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

http://hdl.handle.net/10453/29709