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Vertexcolouring Edgeweightings
 COMBINATORICA
, 2007
"... A weighting w of the edges of a graph G induces a colouring of the vertices of G where the colour of vertex v, denoted cv, is ∑ w(e). We show that the edges of every graph that e∋v does not contain a component isomorphic to K2 can be weighted from the set {1,...,30} such that in the resulting vertex ..."
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Cited by 11 (0 self)
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A weighting w of the edges of a graph G induces a colouring of the vertices of G where the colour of vertex v, denoted cv, is ∑ w(e). We show that the edges of every graph that e∋v does not contain a component isomorphic to K2 can be weighted from the set {1,...,30} such that in the resulting
Circular colorings of edgeweighted graphs
 J. Graph Theory
, 2003
"... The notion of (circular) colorings of edgeweighted graphs is introduced. This notion generalizes the notion of (circular) colorings of graphs, the channel assignment problem, and several other optimization problems. For instance, its restriction to colorings of weighted complete graphs corresponds ..."
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Cited by 12 (4 self)
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The notion of (circular) colorings of edgeweighted graphs is introduced. This notion generalizes the notion of (circular) colorings of graphs, the channel assignment problem, and several other optimization problems. For instance, its restriction to colorings of weighted complete graphs corresponds
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
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Cited by 741 (15 self)
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An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
A distributed algorithm for minimumweight spanning trees
, 1983
"... A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm and exchange ..."
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Cited by 443 (3 self)
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A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm
A computational approach to edge detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1986
"... AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal ..."
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Cited by 4621 (0 self)
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AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Optimal Layout of EdgeWeighted Forests
, 1998
"... The layout problem for trees with weighted edges is motivated by the design of very large scale integrated circuits. Some of the nodes are fixed and the object is to position the remainder so that the total weighted edge cost is minimized. The cost of each edge is the product of its weight and its l ..."
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The layout problem for trees with weighted edges is motivated by the design of very large scale integrated circuits. Some of the nodes are fixed and the object is to position the remainder so that the total weighted edge cost is minimized. The cost of each edge is the product of its weight and its
LINEAR SYSTEMS ON EDGEWEIGHTED GRAPHS
"... Abstract. Let R be any subring of the reals. We present a generalization of linear systems on graphs where divisors are Rvalued functions on the set of vertices and graph edges are permitted to have nonnegative weights in R. Using this generalization, we provide an independent proof of a RiemannRo ..."
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Abstract. Let R be any subring of the reals. We present a generalization of linear systems on graphs where divisors are Rvalued functions on the set of vertices and graph edges are permitted to have nonnegative weights in R. Using this generalization, we provide an independent proof of a Riemann
Deterministic edgeweights in increasing tree families.
, 2008
"... In this work we study edge weights for two specific families of increasing trees, which include binary increasing trees and plane oriented recursive trees as special instances, where planeoriented recursive trees serve as a combinatorial model of scalefree random trees given by the m = 1 case of t ..."
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In this work we study edge weights for two specific families of increasing trees, which include binary increasing trees and plane oriented recursive trees as special instances, where planeoriented recursive trees serve as a combinatorial model of scalefree random trees given by the m = 1 case
Results 1  10
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