Total Margin Algorithms in Support Vector Machines
Summary :
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
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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://global.ieice.org/en_transactions/information/10.1587/e87-d_5_1223/_p
Copy
@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},}
Copy
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 -
English
