Transfer Incremental Learning for Pattern Classification

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dc.contributor.author Zhu, Z
dc.contributor.author Zhu, X
dc.contributor.author Gua, Y
dc.contributor.author Xua, X
dc.contributor.editor Huang, XJ
dc.contributor.editor Jones, G
dc.contributor.editor Koudas, N
dc.contributor.editor Wu, X
dc.contributor.editor Collins-Thompson, K
dc.date.accessioned 2012-02-02T11:11:43Z
dc.date.issued 2010-01
dc.identifier.citation Proceedings of the 19th ACM Conference on Information and Knowledge Management & Co-Located Workshops (CIKM 2010), 2010, pp. 1709 - 1712
dc.identifier.isbn 978-1-4503-0099-5
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16696
dc.description.abstract Traditional machine learning methods, such as Support Vector Machines (SVMs), usually assume that training and test data share the same distributions. Due to the inherent dynamic data nature, it is often observed that (1) the volumes of the training data may gradually grow; and (2) the existing and the newly arrived samples may be subject to different distributions or learning tasks. In this paper, we propose a Transfer Incremental Support Vector Machine(TrISVM), with the objective of tackling changes in data volumes and learning tasks at the same time. By using new updating rules to calculate the inverse matrix, TrISVM solves the existing incremental learning problem more efficiently, especially for high dimensional data. Furthermore, when using new samples to update the existing models, TrISVM employs sample-based weight adjustment procedures to ensure that the concept transferring between auxiliary and target samples can be leveraged to fulfill the transfer learning goal. Experimental results on real-world data sets demonstrate that TrISVM achieves better efficiency and prediction accuracy than both incremental-learning and transfer-learning based methods. In addition, the results also show that TrISVM is able to achieve bidirectional knowledge transfer between two similar tasks.
dc.publisher ACM
dc.relation.isbasedon 10.1145/1871437.1871710
dc.subject Machine learning, support vector machines, incremental learning, transfer learning
dc.subject Machine learning, support vector machines, incremental learning, transfer learning
dc.title Transfer Incremental Learning for Pattern Classification
dc.type Conference Proceeding
dc.parent Proceedings of the 19th ACM Conference on Information and Knowledge Management & Co-Located Workshops (CIKM 2010)
dc.journal.number en_US
dc.publocation USA en_US
dc.publocation United States
dc.publocation USA
dc.publocation USA
dc.publocation USA
dc.publocation USA
dc.publocation USA
dc.identifier.startpage 1709 en_US
dc.identifier.endpage 1712 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.conference International Conference on Electrical Machines and Systems
dc.conference ACM Conference on Information and Knowledge Managem
dc.conference ACM Conference on Information and Knowledge Managem
dc.conference ACM Conference on Information and Knowledge Managem
dc.conference ACM Conference on Information and Knowledge Managem
dc.conference ACM Conference on Information and Knowledge Managem
dc.for 150301 Business Information Management (Incl. Records, Knowledge and Information Management, and Intelligence)
dc.personcode 0000066566 en_US
dc.personcode 107283 en_US
dc.personcode 0000066567 en_US
dc.personcode 0000066568 en_US
dc.percentage 100 en_US
dc.classification.name Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom ACM Conference on Information and Knowledge Managem en_US
dc.date.activity 20101026 en_US
dc.date.activity 2010-10-10
dc.date.activity 2010-10-26
dc.date.activity 2010-10-26
dc.date.activity 2010-10-26
dc.date.activity 2010-10-26
dc.date.activity 2010-10-26
dc.location.activity Toronto, Ontario, Canada en_US
dc.location.activity Korea
dc.location.activity Toronto, Ontario, Canada
dc.location.activity Toronto, Ontario, Canada
dc.location.activity Toronto, Ontario, Canada
dc.location.activity Toronto, Ontario, Canada
dc.location.activity Toronto, Ontario, Canada
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning en_US
dc.description.keywords Delay , Mathematical model , Predictive models , Rotors , Stators , Switches , Torque
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning
dc.staffid en_US
pubs.embargo.period Not known
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 - Quantum Computation and Intelligent Systems


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