the Zollverein's economic impact through a study of urban population and its growth in the German state of Saxony. A model of the effect of market access on urban growth is combined with an extensive data set on town populations in Saxony and its neighbors as well as an improved distance measure based

The Hausdorff distance measures the extent to which each point of a `model' set lies near some point of an `image' set and vice versa. Thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. In this paper we provide

Our goal is to recognize human actions at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatio-temporal volume for each stabilized human figure, and an associated similarity measure

Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a non-trivial problem. In this paper, we study various distance measures and their effect on different clustering techniques. In addition to the standard Euclidean distance

their own coordinates, these approaches allow end hosts to compute their inter-host distances as soon as they discover each other. Moreover coordinates are very efficient in summarizing inter-host distances, making these approaches very scalable. By performing experiments using measured Internet distance

examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in R , learns a distance metric over R that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us

Abstract- Distance measures are used extensively in data mining and other types of data analysis. Such measures assume it is possible to compute for each pair of domain objects their mutual distance. Much of the research in distance measures concentrates on objects either in attribute

This paper describes the development and use of an interface for visually evaluating distance measures. The combination of multidimensional scaling plots, histograms and tables allows for different stages of overview and detail. The interdisciplinary project Rule-based search in text databases

Many learning algorithms, especially nonparametric ones, use distance measures as a source of prior knowledge about the domain. This paper shows how the work of Baxter and Yianilos provides a formal equivalence between distance measures and prior probability distributions in Bayesian inference

. We introduce a distance measure based on the idea that two vectors are considered similar if they lead to similar predictive probability distributions. The suggested approach avoids the scaling problem inherent to many alternative techniques as the method automatically transforms the original