Optimum Gabor filter design and local binary patterns for texture segmentation
Pattern Recognition Letters
Evaluation of k-Nearest Neighbor classifier performance for direct marketing
Expert Systems with Applications: An International Journal
Local class boundaries for support vector machine
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
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The typical nonparametric method of pattern recognition "k-nearest neighbor rule (kNN)" is carried out by counting the labels of k-nearest training samples to a test sample.This method collects the k-nearest neighbors without taking into account a class, and it outputs the class of the test sample by using only the labels of neighborhoods.This paper presents a classifier that outputs the class of a test sample by measuring the distance between the test sample and the average patterns, which are calculated using the k-nearest neighbors belonging to individual classes.A kernel method can be applied to this classifier for improving recognition rates.The performance of the proposed method is verified by experiments with benchmark data sets.