Data reduction in support vector machines by a kernelized ionic interaction model

Hyunsoo Kim; Haesun Park

doi:10.1137/1.9781611972740.56

Data reduction in support vector machines by a kernelized ionic interaction model

Hyunsoo Kim
, Haesun Park

Research output: Contribution to conference › Paper › peer-review

7 Scopus citations

Abstract

A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.

Original language	English (US)
Pages	507-511
Number of pages	5
DOIs	https://doi.org/10.1137/1.9781611972740.56
State	Published - 2004
Externally published	Yes
Event	Proceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States Duration: Apr 22 2004 → Apr 24 2004

Other

Other	Proceedings of the Fourth SIAM International Conference on Data Mining
Country/Territory	United States
City	Lake Buena Vista, FL
Period	4/22/04 → 4/24/04

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10.1137/1.9781611972740.56

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title = "Data reduction in support vector machines by a kernelized ionic interaction model",

abstract = "A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.",

author = "Hyunsoo Kim and Haesun Park",

year = "2004",

doi = "10.1137/1.9781611972740.56",

language = "English (US)",

pages = "507--511",

note = "Proceedings of the Fourth SIAM International Conference on Data Mining ; Conference date: 22-04-2004 Through 24-04-2004",

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AU - Kim, Hyunsoo

AU - Park, Haesun

PY - 2004

Y1 - 2004

N2 - A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.

AB - A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.

UR - https://www.scopus.com/pages/publications/2942532728

UR - https://www.scopus.com/pages/publications/2942532728#tab=citedBy

U2 - 10.1137/1.9781611972740.56

DO - 10.1137/1.9781611972740.56

M3 - Paper

AN - SCOPUS:2942532728

SP - 507

EP - 511

T2 - Proceedings of the Fourth SIAM International Conference on Data Mining

Y2 - 22 April 2004 through 24 April 2004

ER -

Data reduction in support vector machines by a kernelized ionic interaction model

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