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498
OnLine Rankings of Graphs
, 1997
"... A (vertex) kranking of a graph G = (V; E) is a proper vertex coloring ..."
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A (vertex) kranking of a graph G = (V; E) is a proper vertex coloring
Online Ranking of Split Graphs
"... A vertex ranking of a graph G is an assignment of positive integers (colors) to the vertices of G such that each path connecting two vertices of the same color contains a vertex of a higher color. Our main goal is to find a vertex ranking using as few colors as possible. Considering online algorith ..."
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A vertex ranking of a graph G is an assignment of positive integers (colors) to the vertices of G such that each path connecting two vertices of the same color contains a vertex of a higher color. Our main goal is to find a vertex ranking using as few colors as possible. Considering online
1 2 Parallel Algorithm for OnLine Ranking in Trees
"... A node kranking of a graph G = (V, E) is a proper node coloring C: V � {1, 2, …, k} such that any xy path in G with C(x) = C(y) contains an internal node z with C(z) �C(x). In the online version of this problem, the nodes v1, v2, …, vn are coming one by one in an arbitrary order; and only the ed ..."
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A node kranking of a graph G = (V, E) is a proper node coloring C: V � {1, 2, …, k} such that any xy path in G with C(x) = C(y) contains an internal node z with C(z) �C(x). In the online version of this problem, the nodes v1, v2, …, vn are coming one by one in an arbitrary order; and only
Online selection of discriminative tracking features
, 2003
"... This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the ..."
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Cited by 356 (5 self)
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This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking
Learning to Order Things
 Journal of Artificial Intelligence Research
, 1998
"... There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a ..."
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Cited by 409 (12 self)
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that the problem of finding the ordering that agrees best with a preference function is NPcomplete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an online learning algorithm, based on the "
Greedy Rankings and Arank Numbers
, 2009
"... A ranking on a graph is an assignment of positive integers to its vertices such that any path between two vertices of the same rank contains a vertex of strictly larger rank. A ranking is locally minimal if reducing the rank of any single vertex produces a non ranking. A ranking is globally minimal ..."
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one property it satisfies all three. As a consequence of this and known results on arank numbers of paths we improve known upper bounds for online ranking. 1
De featurefrequentiemethode en de classificatie van nederlandse dialecten
 TABU: Bulletin voor Taalwetenschap
, 1988
"... Abstract A node kranking of a graph G = (V, E) is a proper node coloring C: V � {1, 2,…, k} such that any path in G with end nodes x, y fulfilling C(x) = C(y) contains an internal node z with C(z) �C(x). In the online version of this problem, the nodes v1, v2,…, vn are coming one by one in an ar ..."
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Cited by 3 (0 self)
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Abstract A node kranking of a graph G = (V, E) is a proper node coloring C: V � {1, 2,…, k} such that any path in G with end nodes x, y fulfilling C(x) = C(y) contains an internal node z with C(z) �C(x). In the online version of this problem, the nodes v1, v2,…, vn are coming one by one
Results 1  10
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498