Least square support vector machine for large scale dataset

Khanh Nguyen, Trung Le, Vinh Lai, Duy Nguyen, Dat TRAN, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

Support Vector Machine (SVM) is a very well-known tool for classification and regression problems. Many applications require SVMs with non-linear kernels for accurate classification. Training time complexity for SVMs with non-linear kernels is typically quadratic in the size of the training dataset. In this paper, we depart from the very well-known variation of SVM, the so-called Least Square Support Vector Machine, and apply Steepest Sub-gradient Descent method to propose Steepest Sub-gradient Descent Least Square Support Vector Machine (SGD-LSSVM). It is theoretically proven that the convergent rate of the proposed method to gain ε - precision solution is O (log (1/ε)). The experiments established on the large-scale datasets indicate that the proposed method offers the comparable classification accuracies while being faster than the baselines
Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
EditorsAmir Hussain
Place of PublicationUSA
PublisherIEEE
Pages2057-2065
Number of pages9
Volume1
ISBN (Electronic)9781479919604
ISBN (Print)9781479919611
DOIs
Publication statusPublished - 2015
EventInternational Joint Conference on Neural Networks IJCNN 2015 - Killarney, Ireland, Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2015-September

Conference

ConferenceInternational Joint Conference on Neural Networks IJCNN 2015
Abbreviated titleIJCNN 2015
CountryIreland
CityKillarney
Period12/07/1517/07/15

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Support vector machines
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Cite this

Nguyen, K., Le, T., Lai, V., Nguyen, D., TRAN, D., & MA, W. (2015). Least square support vector machine for large scale dataset. In A. Hussain (Ed.), 2015 International Joint Conference on Neural Networks, IJCNN 2015 (Vol. 1, pp. 2057-2065). (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September). USA: IEEE. https://doi.org/10.1109/ijcnn.2015.7280575
Nguyen, Khanh ; Le, Trung ; Lai, Vinh ; Nguyen, Duy ; TRAN, Dat ; MA, Wanli. / Least square support vector machine for large scale dataset. 2015 International Joint Conference on Neural Networks, IJCNN 2015. editor / Amir Hussain. Vol. 1 USA : IEEE, 2015. pp. 2057-2065 (Proceedings of the International Joint Conference on Neural Networks).
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abstract = "Support Vector Machine (SVM) is a very well-known tool for classification and regression problems. Many applications require SVMs with non-linear kernels for accurate classification. Training time complexity for SVMs with non-linear kernels is typically quadratic in the size of the training dataset. In this paper, we depart from the very well-known variation of SVM, the so-called Least Square Support Vector Machine, and apply Steepest Sub-gradient Descent method to propose Steepest Sub-gradient Descent Least Square Support Vector Machine (SGD-LSSVM). It is theoretically proven that the convergent rate of the proposed method to gain ε - precision solution is O (log (1/ε)). The experiments established on the large-scale datasets indicate that the proposed method offers the comparable classification accuracies while being faster than the baselines",
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Nguyen, K, Le, T, Lai, V, Nguyen, D, TRAN, D & MA, W 2015, Least square support vector machine for large scale dataset. in A Hussain (ed.), 2015 International Joint Conference on Neural Networks, IJCNN 2015. vol. 1, Proceedings of the International Joint Conference on Neural Networks, vol. 2015-September, IEEE, USA, pp. 2057-2065, International Joint Conference on Neural Networks IJCNN 2015, Killarney, Ireland, 12/07/15. https://doi.org/10.1109/ijcnn.2015.7280575

Least square support vector machine for large scale dataset. / Nguyen, Khanh; Le, Trung; Lai, Vinh; Nguyen, Duy; TRAN, Dat; MA, Wanli.

2015 International Joint Conference on Neural Networks, IJCNN 2015. ed. / Amir Hussain. Vol. 1 USA : IEEE, 2015. p. 2057-2065 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Nguyen K, Le T, Lai V, Nguyen D, TRAN D, MA W. Least square support vector machine for large scale dataset. In Hussain A, editor, 2015 International Joint Conference on Neural Networks, IJCNN 2015. Vol. 1. USA: IEEE. 2015. p. 2057-2065. (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/ijcnn.2015.7280575