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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
"... 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 43 (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
Approximation Algorithms for the Metric Labeling Problem via a New Linear Programming Formulation
, 2000
"... We consider approximation algorithms for the metric labeling problem. Informally speaking, we are given a weighted graph that specifies relations between pairs of objects drawn from a given set of objects. The goal is to find a minimum cost labeling of these objects where the cost of a labeling is d ..."
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Cited by 77 (1 self)
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We consider approximation algorithms for the metric labeling problem. Informally speaking, we are given a weighted graph that specifies relations between pairs of objects drawn from a given set of objects. The goal is to find a minimum cost labeling of these objects where the cost of a labeling
Approximation Algorithms for the Single Allocation Problem in HubandSpoke Networks and Related Metric Labeling Problems
, 2006
"... The METR technical reports are published as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electron ..."
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The METR technical reports are published as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.
Automatic labeling of semantic roles
 Computational Linguistics
, 2002
"... We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from handannotated training data. 1 ..."
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Cited by 742 (15 self)
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We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from handannotated training data. 1
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
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Cited by 799 (14 self)
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examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in R , learns a distance metric over R that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us
A Metrics Suite for Object Oriented Design
 IEEE Trans. Softw. Eng
, 1994
"... Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to softwar ..."
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Cited by 1079 (3 self)
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to software development, with perhaps the most prominent being objectorientation (00). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a
A HighThroughput Path Metric for MultiHop Wireless Routing
, 2003
"... This paper presents the expected transmission count metric (ETX), which finds highthroughput paths on multihop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. The E ..."
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Cited by 1078 (5 self)
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This paper presents the expected transmission count metric (ETX), which finds highthroughput paths on multihop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Text Classification from Labeled and Unlabeled Documents using EM
 MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
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Cited by 1033 (19 self)
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This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large
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