Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A local mean-based nonparametric classifier
Pattern Recognition Letters
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The k-nearest neighbor classification rule (k-NNR) is a very simple, yet powerful nonparametric classification method. As a variant of the k-NNR, a nonparametric classification method based on the local mean vector has achieved good classification performance. In this paper, a new variant of the k-NNR, a nonparametric classification method based on the local mean vector and the class mean vector has been proposed. Not only the information of the local mean of the knearest neighbors of the unlabeled pattern in each individual class but also the knowledge of the ensemble mean of each individual class are taken into account in this new classification method. The proposed classification method is compared with the k-NNR, and the local mean-based nonparametric classification in terms of the classification error rate on the unknown patterns. Experimental results confirm the validity of this new classification approach.