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Distance measurement
, 2008
"... the Zollverein in the 19 th century 1 Changes in trade institutions, such as the abolishment of tariff barriers, have a potentially strong impact on economic development. The Zollverein, the 1834 customs union between German states, erased borders in much of central Europe. This paper investigates t ..."
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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
Comparing Images Using the Hausdorff Distance
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... 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 ef ..."
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Cited by 659 (10 self)
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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
Recognizing action at a distance
 PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
, 2003
"... 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 spatiotemporal volume for each stabilized human figure, and an associated similarity measure to be us ..."
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Cited by 504 (20 self)
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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 spatiotemporal volume for each stabilized human figure, and an associated similarity measure
On the General Distance Measure, In:
, 2003
"... Abstract: In Walesiak [1993], pp. 4445 the distance measure was proposed, which can be used for the ordinal data. In the paper the proposal of the general distance measure is given. This measure can be used for data measured in ratio, interval and ordinal scale. The proposal is based on the idea o ..."
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Cited by 1 (1 self)
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Abstract: In Walesiak [1993], pp. 4445 the distance measure was proposed, which can be used for the ordinal data. In the paper the proposal of the general distance measure is given. This measure can be used for data measured in ratio, interval and ordinal scale. The proposal is based on the idea
Predicting Internet Network Distance with CoordinatesBased Approaches
 In INFOCOM
, 2001
"... In this paper, we propose to use coordinatesbased mechanisms in a peertopeer architecture to predict Internet network distance (i.e. roundtrip propagation and transmission delay) . We study two mechanisms. The first is a previously proposed scheme, called the triangulated heuristic, which is bas ..."
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Cited by 631 (6 self)
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their own coordinates, these approaches allow end hosts to compute their interhost distances as soon as they discover each other. Moreover coordinates are very efficient in summarizing interhost distances, making these approaches very scalable. By performing experiments using measured Internet distance
An Experiment with Distance Measures for Clustering ∗
"... Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a nontrivial problem. In this paper, we study various distance measures and their effect on different clustering techniques. In addition to the standard Euclidean distance, ..."
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Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a nontrivial problem. In this paper, we study various distance measures and their effect on different clustering techniques. In addition to the standard Euclidean distance
An XML Distance Measure
 Proceedings of The 2005 International Conference on Data Mining (DMIN'05
"... 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 attributevalue represen ..."
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Cited by 5 (2 self)
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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
Visualizing the evaluation of distance measures
"... 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 Rulebased search in text databases with ..."
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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 Rulebased search in text databases
Distance measures as prior probabilities
, 2000
"... 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. The ..."
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Cited by 4 (0 self)
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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
Divergence measures based on the Shannon entropy
 IEEE Transactions on Information theory
, 1991
"... AbstractA new class of informationtheoretic divergence measures based on the Shannon entropy is introduced. Unlike the wellknown Kullback divergences, the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions involved. More importantly, ..."
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Cited by 666 (0 self)
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, their close relationship with the variational distance and the probability of misclassification error are established in terms of bounds. These bounds are crucial in many applications of divergence measures. The new measures are also well characterized by the properties of nonnegativity, finiteness
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