The kNN algorithm remains a popular choice for pattern classification till date due to its non-
parametric nature, easy implementation and the fact that its classification error is bounded …

Nearest neighbor (NN) rule is one of the simplest and the most important methods in pattern
recognition. In this paper, we propose a kernel difference-weighted k-nearest neighbor (KDF …

K-nearest neighbor classification method (KNN), as one of the top 10 algorithms in data
mining, is a very simple and yet effective nonparametric technique for pattern recognition …

Existing classifier designs generally base on vector pattern, hence, when a non-vector
pattern such as a face image as the input to the classifier, it has to be first concatenated to a …

The classical k-NN classifier has been widely used in pattern recognition. However, it does
not take into account the structural information of local samples. This paper presents a novel …

Kernel methods have been widely used in pattern recognition. Many kernel classifiers such
as Support Vector Machines (SVM) assume that data can be separated by a hyperplane in …

This paper proposes an extension to the k Nearest Neighbor algorithm on Feature
Projections, called kNNFP. The kNNFP algorithm has been shown to achieve comparable …

K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern
recognition. However, the KNN-based classification performance is severely affected by the …

The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms in
pattern recognition field because it is very simple to understand but works quite well in …

k nearest neighbor (kNN) method is a popular classification method in data mining and
statistics because of its simple implementation and significant classification performance …