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830,371
The linkprediction problem for social networks
 J. AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
, 2007
"... Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the linkprediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a ne ..."
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Cited by 885 (6 self)
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Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the linkprediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 806 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Induction of Decision Trees
 MACH. LEARN
, 1986
"... The technology for building knowledgebased systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such syste ..."
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Cited by 4313 (4 self)
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The technology for building knowledgebased systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
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Cited by 525 (16 self)
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Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a
Elastically deformable models
 Computer Graphics
, 1987
"... The goal of visual modeling research is to develop mathematical models and associated algorithms for the analysis and synthesis of visual information. Image analysis and synthesis characterize the domains of computer vision and computer graphics, respectively. For nearly three decades, the vision an ..."
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Cited by 872 (19 self)
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, involves the analysis of images to infer object models. Visual modeling takes a unified approach to vision and graphics via modeling that exploits computational physics. In addition to geometry, physicsbased modeling employs forces, torques, internal strain energies, and other physical quantities
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over highdimensional spaces. R ..."
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Cited by 731 (24 self)
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Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over highdimensional spaces
Efficient belief propagation for early vision
 In CVPR
, 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
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Cited by 511 (8 self)
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Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical
Attention, similarity, and the identificationCategorization Relationship
, 1986
"... A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification dat ..."
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Cited by 669 (28 self)
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A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification
An InferenceBased Framework for Multistrategy Learning
 in Machine Learning: A Multistrategy Approach, Volume 4, R.S. Michalski & G. Tecuci (Eds
, 1993
"... This chapter describes a general framework for multistrategy learning. One idea of this framework is to view learning as an inference process and to integrate the elementary inferences that are employed by the singlestrategy learning methods. Another idea is to base learning on building and general ..."
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Cited by 5 (1 self)
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This chapter describes a general framework for multistrategy learning. One idea of this framework is to view learning as an inference process and to integrate the elementary inferences that are employed by the singlestrategy learning methods. Another idea is to base learning on building
Protecting respondents’ identities in microdata release
 In IEEE Transactions on Knowledge and Data Engineering (TKDE
, 2001
"... Today’s globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the ..."
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Cited by 506 (32 self)
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. The approach is based on the definition of kanonymity. A table provides kanonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how kanonymity can be provided without compromising the integrity (or truthfulness
Results 11  20
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830,371