The metric space model abstracts many proximity or similarity problems, where the most frequently considered primitives are range and k-nearest neighbor search, leaving out the similarity join, an extremely important primitive. In fact, despite the great attention that this primitive has received in traditional and even multidimensional databases, little has been done for general metric databas...

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...

In this paper, we consider two applications of distributional similarity measures, probability estimation and prediction of semantic similarity. We investigate whether high performance in one application area is correlated with high performance in the other. This work also provides an evaluation of two state-of-the-art distributional similarity measures and introduces a variant of one. Further,...

The performance of nine different nonparametric regression estimates is empirically compared on ten different real data sets. The number of data points in the real data sets varies between 7900 and 18000, where each real data set contains between 5 and 20 variables. The nonparametric regression estimates include kernel, partitioning, nearest neighbor, additive spline, neural network, penalized ...

Since the points of a line uniquely determine the line, it is safe to talk about lines as sets of points, but for convenience we will often regard them separately. We will talk about projective planes using the usual language of points and lines “lies on”, “passes through”, “collinear”, “concurrent”, etc. The third requirement implies its dual, that there are four lines, no three of which are c...

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)-pairs of points in source and target sets, where each point is the nearest neighbor of the other. BBS has several key f...