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18,604
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1211 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Hierarchical mixtures of experts and the EM algorithm
, 1993
"... We present a treestructured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood ..."
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Cited by 885 (21 self)
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We present a treestructured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood
A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants
 Learning in Graphical Models
, 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
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Cited by 993 (18 self)
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estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible. 1. Introduction The ExpectationMaximization (EM) algorithm finds maximum likelihood parameter estimates in problems
Quantal Response Equilibria For Normal Form Games
 NORMAL FORM GAMES, GAMES AND ECONOMIC BEHAVIOR
, 1995
"... We investigate the use of standard statistical models for quantal choice in a game theoretic setting. Players choose strategies based on relative expected utility, and assume other players do so as well. We define a Quantal Response Equilibrium (QRE) as a fixed point of this process, and establish e ..."
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Cited by 647 (28 self)
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We investigate the use of standard statistical models for quantal choice in a game theoretic setting. Players choose strategies based on relative expected utility, and assume other players do so as well. We define a Quantal Response Equilibrium (QRE) as a fixed point of this process, and establish
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 693 (4 self)
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We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2
Object class recognition by unsupervised scaleinvariant learning
 In CVPR
, 2003
"... We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and ..."
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Cited by 1127 (50 self)
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and relative scale. An entropybased feature detector is used to select regions and their scale within the image. In learning the parameters of the scaleinvariant object model are estimated. This is done using expectationmaximization in a maximumlikelihood setting. In recognition, this model is used in a
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
 Psychological Methods
, 1998
"... This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustn ..."
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Cited by 543 (0 self)
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This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic
The Cache Performance and Optimizations of Blocked Algorithms
 In Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
, 1991
"... Blocking is a wellknown optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This ..."
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Cited by 574 (5 self)
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Blocking is a wellknown optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused
An EnergyEfficient MAC Protocol for Wireless Sensor Networks
, 2002
"... This paper proposes SMAC, a mediumaccess control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use batteryoperated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect senso ..."
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Cited by 1517 (36 self)
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This paper proposes SMAC, a mediumaccess control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use batteryoperated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect
VERY HIGH RESOLUTION INTERPOLATED CLIMATE SURFACES FOR GLOBAL LAND AREAS
, 2005
"... We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered ..."
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Cited by 553 (8 self)
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We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered
Results 1  10
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18,604