This paper explores a memory-based approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which ts a local model to a se...

We present NearptD, a very fast parallel nearest neighbor algorithm and implementation, which has processed 10 points in E and 184 · 10 points in E. It uses 1/5 the space and as little as 1/100 the preprocessing time FLANN (a well-known approximate nearest neighbor program). Up to E, its query time is also faster, by up to a factor of 100. NearptD uses Nvidia Thrust and CUDA in C++ to perform p...

We introduce a novel algorithm for solving the nearest neighbour problem when the query points are known in advance, which is based on Fortune’s plane sweep algorithm. The crucial idea is to use the wavefront for solving the nearest neighbour queries as the Voronoi diagram is being computed, instead of storing it in an auxiliary data structure, as the algorithm presented by Lee and Yang [9] doe...

Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distance function as a “black box”. The structure is able to speed up nearest neighbor searching in a v...

Spatial databases have grown in importance in various fields. Together with them come various types of queries that need to be answered effectively. While queries involving single data set have been studied extensively, join queries on multi-dimensional data like the k-closest pairs and the nearest neighbor joins have only recently received attention. In this paper, we propose a new index struc...

The planar Euclidean version of the traveling salesperson problem requires finding the shortest tour through a two-dimensional array of points. MacGregor and Ormerod (1996) have suggested that people solve such problems by using a global-to-local perceptual organizing process based on the convex hull of the array. We review evidence for and against this idea, before considering an alternative, ...

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...

We propose a nearest point algorithm (NPA) for optimization of a family of Support Vector Machines (SVM) methods. Geometrically, optimizing SVM corresponds to finding the nearest points between two polyhedrons. In classification, the hard margin case corresponds to finding the closest points of the convex hull of each class. The soft margin case corresponds to finding the closest points in the ...

The stationary points of the potential energy function of the φ⁴ model on a two-dimensional square lattice with nearest-neighbor interactions are studied by means of two numerical methods: a numerical homotopy continuation method and a globally convergent Newton-Raphson method. We analyze the properties of the stationary points, in particular with respect to a number of quantities that have bee...

Geometric models impose an upper bound on the number of points that can share the same nearest neighbor. A much more restrictive bound is implied by the assumption that the data points represent a sample from some continuous distribution in a multidimensional Euclidean space. The analysis of 100 data sets shows that most perceptual data satisfy the geometric-statistical bound whereas many conce...