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751
Matching 3D Models with Shape Distributions
, 2001
"... Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, whi ..."
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Cited by 215 (7 self)
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, or model fitting. We find that the dissimilarities between sampled distributions of simple shape functions (e.g., the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g., cars versus airplanes) in a moderately sized database, despite
From local actions to global tasks: Stigmergy and collective robotics
, 1994
"... This paper presents a series of experiments where a group of mobile robots gather 81 randomly distributed objects and cluster them into one pile. Coordination of the agents ’ movements is achieved through stigmergy. This principle, originally developed for the description of termite building behavio ..."
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Cited by 246 (2 self)
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the task decreases for one, two, and three robots respectively, then increases again for groups of four and five agents, due to an exponential increase in the number of interactions between robots which are time consuming and may eventually result in the destruction of existing clusters. We compare our
Multidimensional Scaling of Fuzzy Dissimilarity Data
"... Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as distances between points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are expressed as intervals or fuzzy numbers. Each object is t ..."
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Cited by 8 (1 self)
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Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as distances between points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are expressed as intervals or fuzzy numbers. Each object
mpdissimilarity: A data dependent dissimilarity measure
"... Abstract—Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as `pn ..."
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norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can be exploited to measure dissimilarity between two instances and propose a new data dependent dissimilarity measure called ‘mpdissimilarity’. Rather than relying on geometric distance, it measures
Selfdetermination and persistence in a reallife setting: Toward a motivational model of high school dropout.
 Journal of Personality and Social Psychology,
, 1997
"... The purpose of this study was to propose and test a motivational model of high school dropout. The model posits that teachers, parents, and the school administration's behaviors toward students influence students' perceptions of competence and autonomy. The less autonomy supportive the so ..."
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Cited by 183 (19 self)
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SCHOOL DROPOUT 1165 agreement (7). There was a correlation of .63 between these two items. Finally, in the fifth and last part of the questionnaire, participants were asked to indicate their age, student identification number, gender, and date of birth. Procedure In October, during the fall semester
On measuring the distance between histograms
 PATTERN RECOGNITION
, 2002
"... A distance measure between two histograms has applications in feature selection, image indexing and retrieval, pattern classification and clustering, etc. We propose a distance between sets of measurement values as a measure of dissimilarity of two histograms. The proposed measure has the advantage ..."
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Cited by 67 (7 self)
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A distance measure between two histograms has applications in feature selection, image indexing and retrieval, pattern classification and clustering, etc. We propose a distance between sets of measurement values as a measure of dissimilarity of two histograms. The proposed measure has the advantage
A family of dissimilarity measures between nodes generalizing both the shortestpath and the commutetime distances
 in Proceedings of the 14th SIGKDD International Conference on Knowledge Discovery and Data Mining
"... This work introduces a new family of linkbased dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortestpath (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortestpath d ..."
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Cited by 24 (11 self)
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node to a particular node of interest can be computed efficiently by solving two linear systems of n equations, where n is the number of nodes. On the other hand, the dissimilarity between every couple of nodes is obtained by inverting an n × n matrix. The proposed measure can be used for various graph
The Application of New Concepts of Dissimilarities between Nodes of a Graph to Collaborative Filtering
, 2004
"... This work presents some general procedures for computing dissimilarities between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markovchain model of random walk through the database. The model assigns transition probabilities to the links between ..."
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Cited by 2 (0 self)
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the average commute time, provides a distance measure between any pair of elements. These quantities, representing dissimilarities between any two elements, have the nice property of decreasing when the number of paths connecting two elements increases and when the "length" of any path decreases
Whom You Know Matters: Venture Capital Networks and Investment Performance,
 Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm'slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
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Cited by 138 (8 self)
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this period. 5 As the example in the Appendix illustrates, this method of coding ties produces a binary adjacency matrix. It is possible to construct a valued adjacency matrix accounting not only for the existence of a tie between two VCs but also for the number of times there is a tie between them. While
Longerterm effects of Head Start
 American Economic Review
, 2002
"... Abstract Public early intervention programs like Head Start are often justified as investments in children. Yet nothing is known about the longterm effects of Head Start. This paper draws on unique data from the Panel Study of Income Dynamics to provide new evidence on the effects of Head Start on ..."
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Cited by 131 (5 self)
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as children with the outcomes of the siblings who did not (255 respondents from 100 families). 11 Second, the effects of random measurement errors may be exacerbated in a fixed effects framework. That is, by focussing on differences between siblings within a family, we may difference out much of the true
Results 1  10
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