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October 10, 2008 16:44 WSPC/INSTRUCTION FILE SCHSReview1 International Journal of Pattern Recognition and Artificial Intelligence
 

Summary: October 10, 2008 16:44 WSPC/INSTRUCTION FILE SCHSReview1
International Journal of Pattern Recognition and Artificial Intelligence
c World Scientific Publishing Company
Hyperspherical Prototypes for Pattern Classification
Hatem A. Fayed
Department of Engineering Mathematics and Physics, Cairo University,
Giza, Egypt
h fayed@eng.cu.edu.eg
Amir F. Atiya
Department of Computer Engineering, Cairo University,
Giza, Egypt
amir@alumni.caltech.edu
Sherif R. Hashem
Department of Engineering Mathematics and Physics, Cairo University,
Giza, Egypt
shashem@idsc.gov.eg
The nearest neighbor method is one of the most widely used pattern classification meth-
ods. However its major drawback in practice is the curse of dimensionality. In this paper
we propose a new method to alleviate this problem significantly. In this method, we
attempt to cover the training patterns of each class with a number of hyperspheres. The

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences