Total Margin Algorithms in Support Vector Machines

Min YOON; Yeboon YUN; Hirotaka NAKAYAMA

Total Margin Algorithms in Support Vector Machines

Min YOON, Yeboon YUN, Hirotaka NAKAYAMA

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Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.5 pp.1223-1230

Publication Date: 2004/05/01

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Type of Manuscript: PAPER

Category: Pattern Recognition

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Min YOON, Yeboon YUN, Hirotaka NAKAYAMA, "Total Margin Algorithms in Support Vector Machines" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 5, pp. 1223-1230, May 2004, doi: .
Abstract: Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_5_1223/_p

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@ARTICLE{e87-d_5_1223,
author={Min YOON, Yeboon YUN, Hirotaka NAKAYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Total Margin Algorithms in Support Vector Machines},
year={2004},
volume={E87-D},
number={5},
pages={1223-1230},
abstract={Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Total Margin Algorithms in Support Vector Machines
T2 - IEICE TRANSACTIONS on Information
SP - 1223
EP - 1230
AU - Min YOON
AU - Yeboon YUN
AU - Hirotaka NAKAYAMA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2004
AB - Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.
ER -