A fast nearest neighbor algorithm for pattern classiication is proposed and tested on real data. The patterns (points in d-dimensional Euclidean space) are sorted along a space-lling curve. This way the multidi-mensional problem is compressed to the simplest case of the nearest neighbor search in one dimension.

This paper examines the relationship between road directions and the rectilinear distance in regular point patterns. We derive the distributions of the rectilinear distances to the nearest and the second nearest points in rotated regular point patterns. These distributions demonstrate that road directions significantly affect the rectilinear distances. As an application of the nearest and the s...

In this paper, we ask “Can proximity to towns reduces poverty in rural areas?” For this study we have mapped all the villages in Orissa. The study is based on small area poverty estimation methodology as well as the secondary data covering 47,395 villages and 109 towns. Our main findings are (i) there is a relationship between the distance to nearest town and rural poverty and (ii) poverty leve...

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...

abstract saturated hydraulic conductivity (ks) is needed for many studies related to water and solute transport, but often cannot be measured because of practical and/or cost-related reasons. nonparametric approaches are being used in various fields to estimate continuous variables. one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-nn) algorithm, was introduced and...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...