Most natural language processing tasks can be formulated as the approximated nearest neighbor search problem, such as word analogy, document similarity, machine translation. Take the questionanswering task as an example, given a question as the query, the goal is to search its nearest neighbor in the training dataset as the answer. However, existing methods for approximate nearest neighbor sear...

We consider bounded occurrence (degree) instances of a minimum constraint satisfaction problem MIN-LIN2 and a MIN-BISECTION problem for graphs. MIN-LIN2 is an optimization problem for a given system of linear equations mod 2 to construct a solution that sat-isses the minimum number of them. E3-OCC-MIN-E3-LIN2 is the bounded occurrence (degree) problem restricted as follows: each equation has ex...

In this paper, we introduce a method to obtain the nearest trapezoidal approximation of fuzzy numbers so that preserving conditions expect interval and include the core of a fuzzy number.

This paper explores a method of performing localization on local vision mobile robots. It describes a method of processing images, extracting regions, and comparing those regions against a database of preprocessed images. Localization is achieved using the k-nearest neighbour algorithm as the basis for approximating the current position. Initial results are provided that show the potential of t...

In discrete k-center and k-median clustering, we are given a set of points P in a metric space M , and the task is to output a set C ⊆ P, |C| = k, such that the cost of clustering P using C is as small as possible. For k-center, the cost is the furthest a point has to travel to its nearest center, whereas for k-median, the cost is the sum of all point to nearest center distances. In the fault-t...

in this research, structural and electronic properties of zncdn-1ten clusters (n=1-10) have been studied by formalism of density functional theory and using the projector augmented wave within local density approximation. the structural properties (such as bond length/angle and coordination number), electronic and optical properties (such as binding energy, kohn-sham spectrum and partial charge...

Methods of nearest neighbors are essential in wide range of applications where it is necessary to estimate probability density (e.g. Bayes’s classifier, problems of searching in large databases). This paper contemplates on features of distribution of nearest neighbors’ distances in high-dimensional spaces. It shows that for uniform distribution of points in n-dimensional Euclidean space the dis...