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Capacitated Metric Labeling
, 2011
"... We introduce Capacitated Metric Labeling. As in Metric Labeling, we are given a weighted graph G = (V, E), a label set L, a semimetric dL on this label set, and an assignment cost function φ: V × L → ℜ +. The goal in Metric Labeling is to find an assignment f: V → L that minimizes a particular twoc ..."
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Cited by 1 (0 self)
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We introduce Capacitated Metric Labeling. As in Metric Labeling, we are given a weighted graph G = (V, E), a label set L, a semimetric dL on this label set, and an assignment cost function φ: V × L → ℜ +. The goal in Metric Labeling is to find an assignment f: V → L that minimizes a particular two
The hardness of metric labeling
 IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS’04
, 2004
"... The Metric Labeling problem is an elegant and powerful mathematical model capturing a wide range of classification problems. The input to the problem consists of a set of labels and a weighted graph. Additionally, a metric distance function on the labels is defined, and for each label and each verte ..."
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Cited by 15 (3 self)
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The Metric Labeling problem is an elegant and powerful mathematical model capturing a wide range of classification problems. The input to the problem consists of a set of labels and a weighted graph. Additionally, a metric distance function on the labels is defined, and for each label and each
NonMetric Label Propagation
"... In many applications nonmetric distances are better than metric distances in reflecting the perceptual distances of human beings. Previous studies on nonmetric distances mainly focused on supervised setting and did not consider the usefulness of unlabeled data. In this paper, we present probably t ..."
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Cited by 2 (1 self)
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the first study of label propagation on graphs induced from nonmetric distances. The challenge here lies in the fact that the triangular inequality does not hold for nonmetric distances and therefore, a direct application of existing label propagation methods will lead to inconsistency and conflict. We
On Earthmover Distance, Metric Labeling, and 0Extension
, 2006
"... We study the fundamental classification problems 0Extension and Metric Labeling. 0Extension is closely ..."
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Cited by 8 (0 self)
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We study the fundamental classification problems 0Extension and Metric Labeling. 0Extension is closely
Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields
 IN IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE
, 1999
"... In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have about the problem. An active line of research in this area is concerned with classification when one has information about pa ..."
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Cited by 197 (2 self)
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that optimizes a combinatorial function consisting of assignment costs  based on the individual choice of label we make for each object  and separation costs  based on the pair of choices we make for two "related" objects. We formulate a general classification problem of this type, the metric
A Theory of Metric Labelled Transition Systems
 Papers on General Topology and Applications: 11th Summer Conference at the University of Southern Maine, volume 806 of Annals of the New York Academy of Sciences
, 1995
"... Labelled transition systems are useful for giving semantics to programming languages. Kok and Rutten have developed some theory to prove semantic models defined by means of labelled transition systems to be equal to other semantic models. Metric labelled transition systems are labelled transition sy ..."
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Cited by 5 (0 self)
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Labelled transition systems are useful for giving semantics to programming languages. Kok and Rutten have developed some theory to prove semantic models defined by means of labelled transition systems to be equal to other semantic models. Metric labelled transition systems are labelled transition
An LP Formulation and Approximation Algorithms for the Metric Labeling Problem
, 2004
"... We consider approximation algorithms for the metric labeling problem. This problem was introduced in a paper by Kleinberg and Tardos [26], and captures many classification problems that arise in computer vision and related fields. They gave an O(log k log log k)approximation for the general case whe ..."
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We consider approximation algorithms for the metric labeling problem. This problem was introduced in a paper by Kleinberg and Tardos [26], and captures many classification problems that arise in computer vision and related fields. They gave an O(log k log log k)approximation for the general case
A linear programming formulation and approximation algorithms for the metric labeling problem
 SIAM J. DISCRETE MATH
, 2005
"... We consider approximation algorithms for the metric labeling problem. This problem was introduced in a paper by Kleinberg and Tardos [J. ACM, 49 (2002), pp. 616–630] and captures many classification problems that arise in computer vision and related fields. They gave an O(log k log log k) approximat ..."
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Cited by 44 (1 self)
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We consider approximation algorithms for the metric labeling problem. This problem was introduced in a paper by Kleinberg and Tardos [J. ACM, 49 (2002), pp. 616–630] and captures many classification problems that arise in computer vision and related fields. They gave an O(log k log log k
Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation
 IN ICML
, 2006
"... Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem. An additional convex relaxation of the quadratic approximation is shown to have additive approximation guarantees that ..."
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Cited by 53 (2 self)
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Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem. An additional convex relaxation of the quadratic approximation is shown to have additive approximation guarantees
Metric Labeling and Semimetric Embedding for Protein Annotation Prediction
 In Research in Computational Molecular Biology
, 2011
"... Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These prediction techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, these predictive tasks are modeled as ..."
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Cited by 2 (0 self)
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functions can improve the quality of function prediction techniques. In this paper, we explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from
Results 1  10
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105,812