In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nearby training samples from the class. When such regions lie close to inter-class boundaries, the nearest neighbors of a query may lie in the wrong class, thus leading to errors in the Nearest Neighbor classification rule. The K-local...

background: this study explores the level of occupational stress and main sources of occupational and personal stress among rural health workers (rhw) of the health network of mashhad district. method: the first part of the occupational stress inventory (osi) (called the occupational roles questionnaire (orq)) was used to measure the rural health workers occupational stress (n=172). a number of...

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the distance metric for the input data patterns. Distance Metric learning is to learn a distance metric for the input space of data from a given collection of pair of similar/dissimilar points that preserves the distance relation among the training data. In recent years, many studies have demonstrated, both empi...

One of the fundamental quantities which statistically characterizes a random system of interacting particles is the nearest-neighbor distribution function. We present computer-simulation results for two different types of nearest-neighbor distribution functions for random distributions of identical impenetrable (hard) spheres. We also report, for such systems, computer-simulation data for close...

A new edited k-nearest neighbor (k}NN) rule is proposed. For every sample y in the edited reference set, all the kor (k#l)-nearest neighbors of y must be in the class to which y belongs. Here l denotes the number of samples which tie with the kth nearest neighbor of y with respect to the distance from y. The performance of the rule proposed has been investigated using three classi"cation exampl...

A theory for tunneling between a real surface and a model probe tip, applicable to the "scanning tunneling microscope" is presented. The tunneling current is found to be proportional to the local density of states of the surface, at the position of the tip. It can be shown that the tunneling conductance has exponential dependence on the distance of the nearest approach to the surface

Using tools from numerical algebraic geometry, we propose a phylogenetic reconstruction algorithm which uses the distance to the nearest point on the phylogenetic model to select the tree of best fit. This method allows for the reconstruction of branch lengths and for hypothesis testing. A data-dependent statistical test identifies correctly reconstructed quartet trees 100% of the time when the...

Search for nearest neighbor is the main source of computation in most clustering algorithms. We propose the use of nearest neighbor graph for reducing the number of candidates. The number of distance calculations per search can be reduced from O(N) to O(k) where N is the number of clusters, and k is the number of neighbors in the graph. We apply the proposed scheme within agglomerative clusteri...