Maximal margin learning vector quantisation

Trung Le; Dat Tran; Van Nguyen; Wanli Ma

doi:10.1109/IJCNN.2013.6706940

Maximal margin learning vector quantisation

Trung Le, Dat Tran, Van Nguyen, Wanli Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.

Original language	English
Title of host publication	2013 International Joint Conference on Neural Networks, IJCNN 2013
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Print)	9781467361293
DOIs	https://doi.org/10.1109/IJCNN.2013.6706940
Publication status	Published - 1 Dec 2013
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2013 - Dallas, United States of America Duration: 4 Aug 2013 → 9 Aug 2013 https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding (Proceedings)

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	IEEE International Joint Conference on Neural Networks 2013
Abbreviated title	IJCNN 2013
Country/Territory	United States of America
City	Dallas
Period	4/08/13 → 9/08/13
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding (Proceedings)

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10.1109/IJCNN.2013.6706940

Cite this

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title = "Maximal margin learning vector quantisation",

abstract = "Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.",

author = "Trung Le and Dat Tran and Van Nguyen and Wanli Ma",

year = "2013",

month = dec,

day = "1",

doi = "10.1109/IJCNN.2013.6706940",

language = "English",

isbn = "9781467361293",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

booktitle = "2013 International Joint Conference on Neural Networks, IJCNN 2013",

address = "United States of America",

note = "IEEE International Joint Conference on Neural Networks 2013, IJCNN 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

url = "https://ieeexplore.ieee.org/xpl/conhome/6691896/proceeding",

}

Le, T, Tran, D, Nguyen, V & Ma, W 2013, Maximal margin learning vector quantisation. in 2013 International Joint Conference on Neural Networks, IJCNN 2013., 6706940, Proceedings of the International Joint Conference on Neural Networks, IEEE, Institute of Electrical and Electronics Engineers, IEEE International Joint Conference on Neural Networks 2013, Dallas, Texas, United States of America, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6706940

Maximal margin learning vector quantisation. / Le, Trung; Tran, Dat; Nguyen, Van et al.
2013 International Joint Conference on Neural Networks, IJCNN 2013. IEEE, Institute of Electrical and Electronics Engineers, 2013. 6706940 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Maximal margin learning vector quantisation

AU - Le, Trung

AU - Tran, Dat

AU - Nguyen, Van

AU - Ma, Wanli

PY - 2013/12/1

Y1 - 2013/12/1

N2 - Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.

AB - Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.

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DO - 10.1109/IJCNN.2013.6706940

M3 - Conference Paper

AN - SCOPUS:84893615529

SN - 9781467361293

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - IEEE International Joint Conference on Neural Networks 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

Maximal margin learning vector quantisation

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