In this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the...

OBJECTIVES We evaluated the spatial accessibility of large "chain" supermarkets in relation to neighborhood racial composition and poverty. METHODS We used a geographic information system to measure Manhattan block distance to the nearest supermarket for 869 neighborhoods (census tracts) in metropolitan Detroit. We constructed moving average spatial regression models to adjust for spatial aut...

In this paper, we propose a method, called the nearest feature midpoint (NFM), for pattern classification. Any pair of feature points of the same class is generalized by the feature midpoint (FM) between them. Hence the representational capacity of available prototypes can be expanded. The classification is determined by the nearest distance from the query feature point to each FM. This paper c...

There are several constants and hyperparameters to be specified in the Bayesian model. To set the GPS position error covariance matrix Σ, we calculate the minimum distance from each GPS location in the data to the nearest arc. Assuming that the error is radially symmetric, that the vehicle was on the nearest arc when it generated the GPS point, and approximating that arc locally by a straight l...

Collection of appropriate qualitative and quantitative data is necessary for proper management and planning. Used the suitable inventory methods is necessary and accuracy of sampling methods dependent the inventory net and number of sample point. Nearest neighbor sampling method is a one of distance methods and calculated by three equations (Byth and Riple, 1980; Cotam and Curtis, 1956 and Cota...

The k-nearest neighbour (KNN) technique is a simple yet effective method for classification. In this paper, we propose an efficient weighted nearest neighbour classification algorithm, called PINE, using vertical data representation. A metric called HOBBit is used as the distance metric. The PINE algorithm applies a Gaussian podium function to set weights to different neighbours. We compare PIN...

Distances of several nearest neighbors of a given point in a multidimensional space play an important role in some tasks of data mining. Here we analyze these distances as random variables defined to be functions of a given point and its k-th nearest neighbor. We prove that if there is a constant q such that the mean k-th neighbor distance to this constant power is proportional to the near neig...

The relationship between directional derivatives of generalized distance functions and the existence of generalized nearest points in Banach spaces is investigated. Let G be any nonempty closed subset in a compact locally uniformly convex Banach space. It is proved that if the one-sided directional derivative of the generalized distance function associated to G at x equals to 1 or −1, then the ...

Let complex matrices $A$ and $B$ have the same sizes. Using the singular value decomposition, we characterize the $g$-inverse $B^{(1)}$ of $B$ such that the distance between a given $g$-inverse of $A$ and the set of all $g$-inverses of the matrix $B$ reaches minimum under the unitarily invariant norm. With this result, we derive additive and multiplicative perturbation bounds of the nearest per...