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access icon free New SV selection strategy and local–global regularisation method for improving online SVM learning

  • Author(s): Tinglong Tang 1 ; Shengyong Chen 2 ; Jake Luo 3
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  • Source: Volume 54, Issue 12, 14 June 2018, p. 735 – 736
    DOI:  10.1049/el.2018.0765 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Received 05/03/2018, Published 14/05/2018

During the online learning process of support vector machines (SVMs), when a newly added sample is violating the Karush–Kuhn–Tucker) conditions, the new sample should be a new SV and transfer the old samples between the SVs and the non-SVs. Normally, the performance of an SVM model is decided by the SVs, and the model should be updated by the newly added SVs; therefore, the selection of high-quality candidate SVs will lead to a better learning accuracy, whereas low-quality candidate SVs may result in low learning efficiency and unnecessary updating. A new strategy is proposed to select the candidate SVs. SVs are selected according to two new criteria: the importance and the informativeness criteria. Furthermore, a mixed local–global regularisation method is applied during the online learning process to improve the penalty coefficients. Experiment results show that the proposed algorithm can achieve a better performance with a faster speed and a higher accuracy when compared with traditional methods.

Inspec keywords: learning (artificial intelligence); support vector machines

Other keywords: mixed local–global regularisation method; online SVM learning; SVM model; support vector machines; SV selection strategy; penalty coefficients; Karush–Kuhn–Tucker conditions

Subjects: Learning in AI (theory)

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