Results 1  10
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2,197
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
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Cited by 660 (8 self)
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in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subsetsubset matching. A leastsquares technique is used to estimate 3D motion from the point
Propensity Score Matching Methods For NonExperimental Causal Studies
, 2002
"... This paper considers causal inference and sample selection bias in nonexperimental settings in which: (i) few units in the nonexperimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because uni ..."
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Cited by 714 (3 self)
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This paper considers causal inference and sample selection bias in nonexperimental settings in which: (i) few units in the nonexperimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
, 2002
"... Matching elements of two data schemas or two data instances plays a key role in data warehousing, ebusiness, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (sch ..."
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Cited by 592 (12 self)
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(schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1865 (43 self)
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is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leaveoneout method and the VC
Receiverdriven Layered Multicast
, 1996
"... State of the art, realtime, rateadaptive, multimedia applications adjust their transmission rate to match the available network capacity. Unfortunately, this sourcebased rateadaptation performs poorly in a heterogeneous multicast environment because there is no single target rate — the conflicti ..."
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Cited by 737 (22 self)
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State of the art, realtime, rateadaptive, multimedia applications adjust their transmission rate to match the available network capacity. Unfortunately, this sourcebased rateadaptation performs poorly in a heterogeneous multicast environment because there is no single target rate
Efficient Clustering of HighDimensional Data Sets with Application to Reference Matching
, 2000
"... Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a sm ..."
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Cited by 338 (15 self)
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technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common
A filtering algorithm for constraints of difference in CSPs
 Proceedings of AAAI’94, the 12th (US) National Conference on Artificial Intelligence
, 1994
"... Abstract Many reallife Constraint Satisfaction Problems (CSPs) involve some constraints similar to the alldifferent constraints. These constraints are called constraints of difference. They are defined on a subset of variables by a set of tuples for which the values occuring in the same tuple are ..."
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Cited by 378 (6 self)
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are all different. In this paper, a new filtering algorithm for these constraints is presented. It achieves the generalized arcconsistency condition for these nonbinary constraints. It is based on matching theory and its complexity is low. In fact, for a constraint defined on a subset of p variables
Matching Events in a Contentbased Subscription System
, 2003
"... Contentbased subscription systems are an emerging alternative to traditional publishsubscribe systems, because they permit more flexible subscriptions along multiple dimensions. In these systems, each subscription is a predicate which may test arbitrary attributes within an event. However, the mat ..."
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Cited by 303 (8 self)
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, the matching problem for contentbased systems  determining for each event the subset of all subscriptions whose predicates match the event  is still an open problem. We present an efficient, scalable solution to the matching problem. Our solution has an expected time complexity that is sub
Tree Pattern Matching to Subset Matching in Linear Time
 IN SIAM J. ON COMPUTING
, 2000
"... This paper is the first of two papers describing an O (n polylog(m)) time algorithm for the Tree Pattern Matching problem on a pattern of size m and a text of size n. In this paper, we show an O(n+m) time Turing reduction from the Tree Pattern Matching problem to another problem called the Subset Ma ..."
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Cited by 6 (2 self)
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This paper is the first of two papers describing an O (n polylog(m)) time algorithm for the Tree Pattern Matching problem on a pattern of size m and a text of size n. In this paper, we show an O(n+m) time Turing reduction from the Tree Pattern Matching problem to another problem called the Subset
Fast Algorithms for Subset Matching and Tree Pattern Matching
, 1997
"... This paper describes an O(s log³ s) time deterministic algorithm, an O(s ) randomized Las Vegas algorithm, and an O(s log s) time randomized Monte Carlo algorithm for the Subset Matching problem. Here, s is the sum of the sizes of the text and pattern sets. A variant of ..."
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This paper describes an O(s log³ s) time deterministic algorithm, an O(s ) randomized Las Vegas algorithm, and an O(s log s) time randomized Monte Carlo algorithm for the Subset Matching problem. Here, s is the sum of the sizes of the text and pattern sets. A variant of
Results 1  10
of
2,197