Abstract The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective supervised learning techniques, has often been used in data classification problems but rarely regression settings. This paper introduces a new, more general model. Generalization is based on usage Minkowski distance instead usual Euclidean distance. not optimal choice for practical problems, better...

We study configurations of points on the unit sphere that minimize potential energy for a broad class of potential functions (viewed as functions of the squared Euclidean distance between points). Call a configuration sharp if there are m distances between distinct points in it and it is a spherical (2m− 1)-design. We prove that every sharp configuration minimizes potential energy for all compl...

An Euclidean graph associated with a molecule is defined by a weighted graph with adjacency matrix M = [dij], where for ij, dij is the Euclidean distance between the nuclei i and j. In this matrix dii can be taken as zero if all the nuclei are equivalent. Otherwise, one may introduce different weights for distinct nuclei. Balaban introduced some monster graphs and then Randic computed complexit...

In this paper a new synthesis for circuit design of Euclidean distance calculation is presented. The circuit is implemented based on a simple two-quadrant squarer/divider block. The circuit that employs floating gate MOS (FG-MOS) transistors operating in weak inversion region, features low circuit complexity, low power (<20uW), low supply voltage (0.5V), two quadrant input current, wide dyn...

Abstract In this paper, we present a drone-based delivery system that assumes to deal with mixed-area, i.e., two areas, one rural and urban, placed side-by-side. the mixed-areas, called EM-grids, distances are measured different metrics, shortest path between destinations concatenates Euclidean Manhattan metrics. Due payload constraints, drone serves single customer at time returning back dispa...

This paper presents a new action recognition approach based on local spatio-temporal features. The main contributions of our approach are twofold. First, a new local spatio-temporal feature is proposed to represent the cuboids detected in video sequences. Specifically, the descriptor utilizes the covariance matrix to capture the self-correlation information of the low-level features within each...

In this work, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as Harmonic distance and Geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate model than the conventional Euclidean distance and Manhattan distance. Becaus...

We present an approach to estimating distances in sensor networks. It works by counting common neighbors, high values indicating closeness. Such distance estimates are needed in many self-localization algorithms. Other than many other approaches, ours does not rely on special equipment in the devices.