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Divide and conquer algorithm based support vector machine for massive data analysis
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대용량 자료의 분류분석을 위한 분할정복 서포터 벡터 머신

논문 기본 정보

Type
Academic journal
Author
Sungwan Bang (육군사관학교) Seokwon Han (육군사관학교) Jaeoh Kim (육군본부)
Journal
The Korean Data and Information Science Society Journal of the Korean Data And Information Science Society Vol.32 No.3 KCI Excellent Accredited Journal
Published
2021.5
Pages
463 - 473 (11page)
DOI
10.7465/jkdi.2021.32.3.463

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Divide and conquer algorithm based support vector machine for massive data analysis
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Abstract· Keywords

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The support vector machine (SVM) has been successfully applied to various classification areas with great flexibility and a high level of classification accuracy. However, it is infeasible to use the SVM in analyzing massive data because of its significant computational problems such as the limitation of computer primary memory. To overcome such a problem, we propose a divide and conquer based SVM (DC-SVM) method. The proposed DC-SVM divides the entire training data into a few subsets, and applies the SVM onto each subset to estimate its classifier. And then DC-SVM obtains the final classifier by aggregating all classifiers from subsets. Simulation studies are presented to demonstrate satisfactory performance of the proposed method.
#대용량 자료 #분할정복 알고리즘 #서포터 벡터 머신 #이차계획법 #Divide and conquer #massive data #quadratic programming #support vector machine

Contents

요약
1. 머리말
2. 분할정복 서포트 벡터 머신
3. 모의실험
4. 실제 자료분석
5. 결론
References
Abstract

References (17)

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방성완 / 2020 / 대용량 자료의 분석을 위한 분할정복 커널 분위수 회귀모형 / 응용통계연구 33(5):569~578 dbpia Brieman, L. / 1996 / Bagging predictors / Machine Learning 26:123~140 google schola Chen, X. / 2019 / Quantile regression under memory constraint / Annals of Statistics 47:3244~3273 google schola Chen, X. / 2014 / A split-and-conquer approach for analysis of extraordinarily large data / Statistica Sinica 24:1655~1684 google schola Chen, L. / 2020 / Quantile regression in big dagta : A divide and conquer based strategy / Computational Statistics and Data Analysis 144:1~17 google schola

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