k Nearest Neighbor (kNN) method is a simple and popular statistical for classification regression. For both regression problems, existing works have shown that, if the distribution of feature vector has bounded support probability density function away from zero in its support, convergence rate standard kNN method, which same all test samples, minimax optimal. On contrary, unbounded we show tha...

In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor...

This paper describes a fully automatic chromosome classification algorithm for Multiplex Fluorescence In-Situ Hybridization(M-FISH) images using supervised parametric and non-parametric techniques. M-FISH is a recently developed chromosome imaging method in which each chromosome is labelled with 5 fluors (dyes) and a DNA stain. The classification problem is modelled as a 25-class 6-feature pixe...

For classifying time series, a nearest-neighbor approach is widely used in practice with performance often competitive with or better than more elaborate methods such as neural networks, decision trees, and support vector machines. We develop theoretical justification for the effectiveness of nearest-neighbor-like classification of time series. Our guiding hypothesis is that in many application...

ÐWe consider a popular approach to multicategory classification tasks: a two-stage system based on a first (global) classifier with rejection followed by a (local) nearest-neighbor classifier. Patterns which are not rejected by the first classifier are classified according to its output. Rejected patterns are passed to the nearest-neighbor classifier together with the top-h ranking classes retu...

We show that a clear formulation of the nearest neighbor decision rule for pattern classification can be found, along with other remarkably modern ideas, in an influential medieval treatise on visual perception authored by Alhazen, one of the major figures in the so-called “Islamic golden age.” To put the work in context we provide also a brief description of some of the salient points of Alhaz...

The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of samples in the training set is high and performance extremely sensitive to outliers. Several attempts of overcoming such drawbacks have been proposed in the pattern recognition field aimed at selecting/gen-erating an adequate subset of prototypes from the training set. The problem addressed in th...