Adaptive pruning algorithm for least squares support vector machine classifier

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dc.contributor.author Yang, X
dc.contributor.author Lu, J
dc.contributor.author Zhang, G
dc.date.accessioned 2012-02-02T11:04:30Z
dc.date.issued 2010-01
dc.identifier.citation Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2010, 14 (7), pp. 667 - 680
dc.identifier.issn 1432-7643
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/15942
dc.description.abstract As a new version of support vector machine (SVM), least squares SVM (LS-SVM) involves equality instead of inequality constraints and works with a least squares cost function. A well-known drawback in the LSSVM applications is that the sparseness is lost. In this paper, we develop an adaptive pruning algorithm based on the bottom-to-top strategy, which can deal with this drawback. In the proposed algorithm, the incremental and decremental learning procedures are used alternately and a small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using this set, one can construct the final classifier. In general, the number of the elements in the support vector set is much smaller than that in the training set and a sparse solution is obtained. In order to test the efficiency of the proposed algorithm, we apply it to eight UCI datasets and one benchmarking dataset. The experimental results show that the presented algorithm can obtain adaptively the sparse solutions with losing a little generalization performance for the classification problems with no-noises or noises, and its training speed is much faster than sequential minimal optimization algorithm (SMO) for the large-scale classification problems with no-noises.
dc.publisher Springer Berlin / Heidelberg
dc.relation.isbasedon 10.1109/ICICTA.2010.104
dc.title Adaptive pruning algorithm for least squares support vector machine classifier
dc.type Journal Article
dc.parent Soft Computing - A Fusion of Foundations, Methodologies and Applications
dc.journal.volume 7
dc.journal.volume 14
dc.journal.number 7 en_US
dc.publocation Germany en_US
dc.identifier.startpage 667 en_US
dc.identifier.endpage 680 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 1702 Cognitive Sciences
dc.personcode 001038
dc.personcode 020014
dc.percentage 100 en_US
dc.classification.name Cognitive Sciences en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.location.activity ISI:000249130600001
dc.description.keywords Support vector machine, Least squares support vector machine, Pruning, Incremental learning, Decremental learning, Adaptive en_US
dc.description.keywords burnout
dc.description.keywords literature review
dc.description.keywords nursing
dc.description.keywords resilience
dc.description.keywords retention
dc.description.keywords workplace adversity
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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