IEICE TRANSACTIONS on Fundamentals
A Fast K Nearest Neighbors Classification Algorithm
Summary :
A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.
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- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.4 pp.961-963
- Publication Date
- 2004/04/01
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- LETTER
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Jeng-Shyang PAN, Yu-Long QIAO, Sheng-He SUN, "A Fast K Nearest Neighbors Classification Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 4, pp. 961-963, April 2004, doi: .
Abstract: A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_4_961/_p
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@ARTICLE{e87-a_4_961,
author={Jeng-Shyang PAN, Yu-Long QIAO, Sheng-He SUN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Fast K Nearest Neighbors Classification Algorithm},
year={2004},
volume={E87-A},
number={4},
pages={961-963},
abstract={A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - A Fast K Nearest Neighbors Classification Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 961
EP - 963
AU - Jeng-Shyang PAN
AU - Yu-Long QIAO
AU - Sheng-He SUN
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2004
AB - A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.
ER -
English
