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Dynamic Pareto Optimal Matching
, 2008
"... We consider the problem of assigning houses to agents, where each agent has his own partial ranking of the houses (i.e., a preference list of a subset of the houses). A matching M is Pareto optimal if there exists no other matching M ′ such that all agents have a house in M ′ not worse than in M and ..."
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Cited by 1 (1 self)
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We consider the problem of assigning houses to agents, where each agent has his own partial ranking of the houses (i.e., a preference list of a subset of the houses). A matching M is Pareto optimal if there exists no other matching M ′ such that all agents have a house in M ′ not worse than in M
Efficient semantic matching
, 2004
"... We think of Match as an operator which takes two graphlike structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into prop ..."
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Cited by 855 (68 self)
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into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class
Linear pattern matching algorithms
 IN PROCEEDINGS OF THE 14TH ANNUAL IEEE SYMPOSIUM ON SWITCHING AND AUTOMATA THEORY. IEEE
, 1972
"... In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear ti ..."
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Cited by 546 (0 self)
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In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear
Matching pursuits with timefrequency dictionaries
 IEEE Transactions on Signal Processing
, 1993
"... AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures t ..."
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Cited by 1671 (13 self)
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matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. We compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected
Optimal Matching Algorithm
"... The optimal matching (OM) algorithm is widely used for sequence analysis in sociology. It has a natural interpretation for discretetime sequences but is also widely used for lifehistory data, which are continuous in time. Lifehistory data are arguably better dealt with in terms of episodes rather ..."
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The optimal matching (OM) algorithm is widely used for sequence analysis in sociology. It has a natural interpretation for discretetime sequences but is also widely used for lifehistory data, which are continuous in time. Lifehistory data are arguably better dealt with in terms of episodes
Shape Matching and Object Recognition Using Shape Contexts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1809 (21 self)
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similar shapes will have similar shape con texts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; reg ularized thin plate splines provide a flexible class of transformation maps
An optimal matching problem
 ESAIM: Control, Optimisation and Calculus of Variations
"... Abstract. Given two measured spaces (X, µ) and (Y, ν), and a third space Z, given two functions u(x, z) and v(x, z), we study the problem of finding two maps s: X → Z and t: Y → Z such that the images s(µ) and t(ν) coincide, and the integral ∫ u(x, s(x))dµ + v(y, t(y))dν is maximal. We ..."
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Cited by 16 (0 self)
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Abstract. Given two measured spaces (X, µ) and (Y, ν), and a third space Z, given two functions u(x, z) and v(x, z), we study the problem of finding two maps s: X → Z and t: Y → Z such that the images s(µ) and t(ν) coincide, and the integral ∫ u(x, s(x))dµ + v(y, t(y))dν is maximal. We
The pyramid match kernel: Discriminative classification with sets of image features
 IN ICCV
, 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernelbased classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
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Cited by 544 (29 self)
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for correspondences – generally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multiresolution histograms and computes a weighted histogram intersection in this space. This “pyramid match” computation is linear
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