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Pattern Recognition 40 (2007) 14981509 www.elsevier.com/locate/pr
 

Summary: Pattern Recognition 40 (2007) 14981509
www.elsevier.com/locate/pr
Self-generating prototypes for pattern classification
Hatem A. Fayeda
, Sherif R. Hashema
, Amir F. Atiyab,
aDepartment of Engineering Mathematics and Physics, Cairo University, Giza, Egypt
bDepartment of Computer Engineering, Cairo University, Giza, Egypt
Received 15 February 2006; received in revised form 15 February 2006; accepted 17 October 2006
Abstract
Prototype classifiers are a type of pattern classifiers, whereby a number of prototypes are designed for each class so as they act as
representatives of the patterns of the class. Prototype classifiers are considered among the simplest and best performers in classification
problems. However, they need careful positioning of prototypes to capture the distribution of each class region and/or to define the class
boundaries. Standard methods, such as learning vector quantization (LVQ), are sensitive to the initial choice of the number and the locations
of the prototypes and the learning rate. In this article, a new prototype classification method is proposed, namely self-generating prototypes
(SGP). The main advantage of this method is that both the number of prototypes and their locations are learned from the training set
without much human intervention. The proposed method is compared with other prototype classifiers such as LVQ, self-generating neural
tree (SGNT) and K-nearest neighbor (K-NN) as well as Gaussian mixture model (GMM) classifiers. In our experiments, SGP achieved
the best performance in many measures of performance, such as training speed, and test or classification speed. Concerning number of
prototypes, and test classification accuracy, it was considerably better than the other methods, but about equal on average to the GMM

  

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

 

Collections: Computer Technologies and Information Sciences