Feature-weighted K-nearest neighbor algorithm with SVM

Zhongshan Daxue Xuebao/Acta Scientiarum Natralium Universitatis Sunyatseni

Volumn 44, Issue 1, 2005, Pages 17-20

  Chen, Zhen Zhou ^a   Li, Lei ^a   Yao, Zheng An ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Distance weighted; Feature weighted; K nearest neighbor; SVM

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; FINITE AUTOMATA; LEARNING SYSTEMS; PATTERN RECOGNITION; REGRESSION ANALYSIS;

FEATURE-WEIGHTED K-NEAREST NEIGHBOR ALGORITHM; K-NEAREST NEIGHBOR ALGORITHM (K-NN); NONPARAMETRIC APPROACH; SAMPLES WEIGHT; SUPPORT VECTOR MACHINE (SVM);

LEARNING ALGORITHMS;

EID: 14844354533     PISSN: 05296579     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. COVER T M, HART P E. Nearest neighbor pattern classification[J]. In Trans IEEE Inform Theory, 1967, IT-13: 21-27.
2. CHO T H, CONNERS R W, ARAMAN P A. A comparison of rule-based, K-nearest neighbor, and neural net classifiers for automation[C]. Proceedings, Developing and Managing Expert System Programs, 1991, 202-209.
3. DUDANI S A. The distance-weighted k-nearest-neighbor rule[J]. IEEE Trans Syst Man Cyber, 1976, 6: 325-327.
4. BURGES J C. A tutorial on support vector machines for pattern recognition[M]. Bell Laboratories, Lucent Technologies, Boston, 1997.
5. KEERTHI S S, SHEVADE S K, BHATTACHARYYA C, et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation, 2001, 13(3): 637-649.
6. LIN C J. A formal analysis of stopping criteria of decomposition methods for support vector machines[J]. IEEE Transaction on Neural Networks, 2002, 13(5): 1045-1052.
7. LEE J H, LIN C J. Automatic model selection for support vector machines[EB/OL]. Available from http://www.csie.ntu.edu.tw/-cjlin/papers.html, 2000.