Learning performance of DAGSVM algorithm based on Markov sampling

Jie Xu; Yang Lu; Bin Zou

doi:10.1109/ICMLC.2015.7340674

Learning performance of DAGSVM algorithm based on Markov sampling

Jie Xu
, Yang Lu
, Bin Zou^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Support vector machine (SVM) is originally designed for 2-class classification problem under the assumption of independent and identically distributed (i.i.d.) sampling. Most classification problems in practice involve multiple categories, hence the SVM has been extended to handle multi-class classification by solving a series of binary classification problems such as the Directed Acyclic Graph SVM (DAGSVM) method. In this paper, we propose the new DAGSVM based on the Markov sampling to replace the classical framework of i.i.d. samples. Numerical studies on the learning performance of the DAGSVM based on Markov sampling for real-world dátasete are presented. Experimental results indicate that the DAGSVM based on Markov sampling yields better learning performance compared to the DAGSVM algorithm based on independent random sampling.

Original language	English
Title of host publication	Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015
Publisher	IEEE
Pages	910-915
Number of pages	6
ISBN (Electronic)	9781467372213, 9781467372206
DOIs	https://doi.org/10.1109/ICMLC.2015.7340674
Publication status	Published - 12 Jul 2015
Event	14th International Conference on Machine Learning and Cybernetics, ICMLC 2015 - Guangzhou, China Duration: 12 Jul 2015 → 15 Jul 2015

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	14th International Conference on Machine Learning and Cybernetics, ICMLC 2015
Country/Territory	China
City	Guangzhou
Period	12/07/15 → 15/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

Directed a-cyclic graph SVM (DAGSVM)
Learning performance
Markov sampling
Multi-class classification

Access to Document

10.1109/ICMLC.2015.7340674

Cite this

@inproceedings{01d1d7cd212e4b5eb7221fff3ade671c,

title = "Learning performance of DAGSVM algorithm based on Markov sampling",

abstract = "Support vector machine (SVM) is originally designed for 2-class classification problem under the assumption of independent and identically distributed (i.i.d.) sampling. Most classification problems in practice involve multiple categories, hence the SVM has been extended to handle multi-class classification by solving a series of binary classification problems such as the Directed Acyclic Graph SVM (DAGSVM) method. In this paper, we propose the new DAGSVM based on the Markov sampling to replace the classical framework of i.i.d. samples. Numerical studies on the learning performance of the DAGSVM based on Markov sampling for real-world d{\'a}tasete are presented. Experimental results indicate that the DAGSVM based on Markov sampling yields better learning performance compared to the DAGSVM algorithm based on independent random sampling.",

keywords = "Directed a-cyclic graph SVM (DAGSVM), Learning performance, Markov sampling, Multi-class classification",

author = "Jie Xu and Yang Lu and Bin Zou",

note = "This work is supported in part by NSFC project (61370002, 61403132). The corresponding author is Bin Zou. Publisher Copyright: {\textcopyright} 2015 IEEE.; 14th International Conference on Machine Learning and Cybernetics, ICMLC 2015 ; Conference date: 12-07-2015 Through 15-07-2015",

year = "2015",

month = jul,

day = "12",

doi = "10.1109/ICMLC.2015.7340674",

language = "English",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE",

pages = "910--915",

booktitle = "Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015",

address = "United States",

}

Xu, J, Lu, Y & Zou, B 2015, Learning performance of DAGSVM algorithm based on Markov sampling. in Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015. Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2, IEEE, pp. 910-915, 14th International Conference on Machine Learning and Cybernetics, ICMLC 2015, Guangzhou, China, 12/07/15. https://doi.org/10.1109/ICMLC.2015.7340674

Learning performance of DAGSVM algorithm based on Markov sampling. / Xu, Jie; Lu, Yang; Zou, Bin.
Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015. IEEE, 2015. p. 910-915 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

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AU - Lu, Yang

AU - Zou, Bin

PY - 2015/7/12

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N2 - Support vector machine (SVM) is originally designed for 2-class classification problem under the assumption of independent and identically distributed (i.i.d.) sampling. Most classification problems in practice involve multiple categories, hence the SVM has been extended to handle multi-class classification by solving a series of binary classification problems such as the Directed Acyclic Graph SVM (DAGSVM) method. In this paper, we propose the new DAGSVM based on the Markov sampling to replace the classical framework of i.i.d. samples. Numerical studies on the learning performance of the DAGSVM based on Markov sampling for real-world dátasete are presented. Experimental results indicate that the DAGSVM based on Markov sampling yields better learning performance compared to the DAGSVM algorithm based on independent random sampling.

AB - Support vector machine (SVM) is originally designed for 2-class classification problem under the assumption of independent and identically distributed (i.i.d.) sampling. Most classification problems in practice involve multiple categories, hence the SVM has been extended to handle multi-class classification by solving a series of binary classification problems such as the Directed Acyclic Graph SVM (DAGSVM) method. In this paper, we propose the new DAGSVM based on the Markov sampling to replace the classical framework of i.i.d. samples. Numerical studies on the learning performance of the DAGSVM based on Markov sampling for real-world dátasete are presented. Experimental results indicate that the DAGSVM based on Markov sampling yields better learning performance compared to the DAGSVM algorithm based on independent random sampling.

KW - Directed a-cyclic graph SVM (DAGSVM)

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KW - Markov sampling

KW - Multi-class classification

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ER -

Learning performance of DAGSVM algorithm based on Markov sampling

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