A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., “find all my nearest gas stations during my route from point s to point e”). The result contains a set of tuples, such that point is the NN of all points in the corresponding interval. Existing methods for continuous nearest neighbor search are based on the repetitiv...

MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed discrete continuous variables. They can be solved using a Branch-and-Bound scheme combining several methods, such as linear programming, interval analysis, and cutting methods. Our goal is to integrate constraint programming techniques in this framework. Firstly, global constraints can be introduced ...

A stochastic approximation to the nearest neighbour (NN) classification rule is proposed. This approximation is called Local Stochastic Competition 0.22). Some corivergence properties of LSC are discussed, and experimental results are presented. The approach shows a great potential for speeding up the codification process, with an affordable loss of codification quality.

Image classification is an important task in computer vision. In this paper, we propose a new image representation based on local feature vectors approximation by the wavelet networks. To extract an approximation of the feature vectors space, a Wavelet Network algorithm based on fast Wavelet is suggested. Then, the K-nearest neighbor (K-NN) classification algorithm is applied on the approximate...

Motivated by applications in computer vision and databases, we introduce and study the Simultaneous Nearest Neighbor Search (SNN) problem. Given a set of data points, the goal of SNN is to design a data structure that, given a collection of queries, finds a collection of close points that are “compatible” with each other. Formally, we are given k query points Q = q1, · · · , qk, and a compatibi...

envelopment analysis (dea) is a very eective method to evaluate the relative eciency of decision-making units (dmus). dea models divided all dmus in two categories: ecient and inecientdmus, and don't able to discriminant between ecient dmus. on the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ...

The references (e.g., Indyk and Motwani, 1999, Datar et al., 2004) contain extensive discussions of the complexity and applications of nearest neighbor algorithms and the motivations for introducing approximations. Complexity does not directly concern us in this paper, so we will not discuss it further; this paper is concerned with the utility and implications of the approximation scheme chosen...

Superconducting phase diagram of the extended Hubbard model supplemented with interaction and hopping terms exceeding nearest neighbour distance in range is analysed systematically at different band-filling and temperature values in a mean-field approximation. The obtained results clearly underline the importance of next-nearest neighbour terms in developing the main superconducting properties ...

I present first results from an in-progress calculation of BK in quenched approximation using overlap fermions. My particular implementation of the overlap uses a kernel with nearest and next-nearest neighbor interactions and HYP-blocked gauge connections. Matching to the continuum NDR regularization is done perturbatively. I present preliminary results at β = 5.9 and 6.1 (lattice spacings 0.12...