A Local Learning Framework Based on Multiple Local Classifiers

BaekSop KIM; HyeJeong SONG; JongDae KIM

A Local Learning Framework Based on Multiple Local Classifiers

BaekSop KIM, HyeJeong SONG, JongDae KIM

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This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.7 pp.1971-1973

Publication Date: 2004/07/01

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Type of Manuscript: LETTER

Category: Pattern Recognition

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BaekSop KIM, HyeJeong SONG, JongDae KIM, "A Local Learning Framework Based on Multiple Local Classifiers" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 7, pp. 1971-1973, July 2004, doi: .
Abstract: This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_7_1971/_p

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@ARTICLE{e87-d_7_1971,
author={BaekSop KIM, HyeJeong SONG, JongDae KIM, },
journal={IEICE TRANSACTIONS on Information},
title={A Local Learning Framework Based on Multiple Local Classifiers},
year={2004},
volume={E87-D},
number={7},
pages={1971-1973},
abstract={This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - A Local Learning Framework Based on Multiple Local Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 1971
EP - 1973
AU - BaekSop KIM
AU - HyeJeong SONG
AU - JongDae KIM
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2004
AB - This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.
ER -