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The geometry of graphs and some of its algorithmic applications
 COMBINATORICA
, 1995
"... In this paper we explore some implications of viewing graphs as geometric objects. This approach offers a new perspective on a number of graphtheoretic and algorithmic problems. There are several ways to model graphs geometrically and our main concern here is with geometric representations that res ..."
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Cited by 524 (19 self)
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that respect the metric of the (possibly weighted) graph. Given a graph G we map its vertices to a normed space in an attempt to (i) Keep down the dimension of the host space and (ii) Guarantee a small distortion, i.e., make sure that distances between vertices in G closely match the distances between
From Data Mining to Knowledge Discovery in Databases.
 AI Magazine,
, 1996
"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in database ..."
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Cited by 538 (0 self)
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in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular realworld applications, specific datamining techniques, challenges involved in realworld applications of knowledge discovery, and current and future
STATISTICAL MAPPING CAPABILITIES AND POTENTIALS OF SAS
"... •SAS, the statistical analysis, data management and graphics system (with SAS/GRAPH) offers several procedures and programming capabilities that provide for the efficient analysis and mapping of geographically based statistical data. Cartographic data is stored in a standard statistical file providi ..."
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•SAS, the statistical analysis, data management and graphics system (with SAS/GRAPH) offers several procedures and programming capabilities that provide for the efficient analysis and mapping of geographically based statistical data. Cartographic data is stored in a standard statistical file
Statistical Mapping of Brain Connectivity
"... Diffusion tensor MRIimaging allows a unique opportunity to observe molecular mobility in living tissues. In brain white matter diffusion shows strong anisotropy and provides exquisite detail on nerve fiber tract organization. If tensor imaging is already a powerful clinical tool, only the trivial ..."
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Diffusion tensor MRIimaging allows a unique opportunity to observe molecular mobility in living tissues. In brain white matter diffusion shows strong anisotropy and provides exquisite detail on nerve fiber tract organization. If tensor imaging is already a powerful clinical tool, only the trivial part of its potential is used. The most advanced application is certainly that of nerve fibertracking.
2 Neuroimages Are Statistical Maps
"... fMRI promises to uncover the functional structure of the brain. I argue, however, that pictures of ‘brain activity ’ associated with fMRI experiments are poor evidence for functional claims. These neuroimages present the results of null hypothesis significance tests performedon fMRIdata. Significanc ..."
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fMRI promises to uncover the functional structure of the brain. I argue, however, that pictures of ‘brain activity ’ associated with fMRI experiments are poor evidence for functional claims. These neuroimages present the results of null hypothesis significance tests performedon fMRIdata. Significance tests alone cannot provide evidence about the functional structure of causally dense systems, including the brain. Instead, neuroimages should be seen as indicating regions where further data analysis is warranted. This additional analysis rarely involves simple significance testing, and so justified skepticism about neuroimages does not provide reason for skepticism about fMRI more generally.
Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping
, 2001
"... The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image of statistics ..."
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Cited by 396 (9 self)
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The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image
Statistical shape influence in geodesic active contours
 In Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC
, 2000
"... A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero l ..."
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Cited by 396 (4 self)
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level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior
ModelDriven Data Acquisition in Sensor Networks
 IN VLDB
, 2004
"... Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor ..."
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Cited by 449 (36 self)
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Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor
A fast and flexible statistical model for largescale population genotype data: Applications to inferring missing genotypes and haplotype phase
 American Journal of Human Genetics
, 2005
"... We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of ..."
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Cited by 408 (10 self)
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We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because
Machine Learning and Statistical MAP Methods
"... Abstract. For machine learning of an inputoutput function f from examples, we show it is possible to define an a priori probability density function on the hypothesis space to represent knowledge of the probability distribution of f, even when the hypothesis space H is large (i.e., nonparametric). ..."
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). This allows extension of maximum a posteriori (MAP) estimation methods nonparametric function estimation. Among other things, the resulting MAPN (MAP for nonparametric machine learning) procedure easily reproduces spline and radial basis function solutions of learning problems. 1
Results 11  20
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