This paper presents a new hybrid classifier that combines the Nearest Neighbor distance based algorithm with the Classification Tree paradigm. The Nearest Neighbor algorithm is used as a preprocessing algorithm in order to obtain a modified training database for the posterior learning of the classification tree structure; experimental section shows the results obtained by the new algorithm; com...

In order to achieve routing in a graph, nodes need to store routing information. In the case of shortest path routing, for a given destination, every node has to store an advice that is an outgoing link toward a neighbor. If this neighbor does not belong to a shortest path then the advice is considered as an error and the node giving this advice will be qualified a liar. This article focuses on...

The “nearest neighbor” relation, or more generally the “k nearest neighbors” relation, defined for a set of points in a metric space, has found many uses in computational geometry and clustering analysis, yet surprisingly little is known about some of its basic properties. In this paper, we consider some natural questions that are motivated by geometric embedding problems. We derive bounds on t...

total fertility rate relates to the number of children that a woman bears in the fertility period of her life. fertility function is one of the most important health and population indicators. this indicator affects social, health, economic and cultural factors. this study is an ecological or correlative one. factors affecting total fertility rate in iran in the year 2000 are studied in this pa...

Spatial variability among experimental plots may be a relevant problem in field genotype experiments, especially when a large number of entries are involved. Whenever variability cannot be controlled by blocking, nearest neighbor methods can be helpful. The purpose of this study is to evaluate nearest neighbor methods and to present other control methods for intense soil heterogeneity. Three ex...

ÐNearest-neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest-neighbor rule. We propose a locally adaptive nearest-neighbor classification method to try to minimize bias. We use a Chi-s...