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» Approximate Expectation Maximization
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FOCS
2004
IEEE
15 years 3 months ago
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
We consider a stochastic variant of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary d...
Brian C. Dean, Michel X. Goemans, Jan Vondrá...
NIPS
1998
15 years 17 days ago
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
Tony Jebara, Alex Pentland
ALGORITHMICA
2006
161views more  ALGORITHMICA 2006»
14 years 11 months ago
The Expected Size of the Rule k Dominating Set
Dai, Li, and Wu proposed Rule k, a localized approximation algorithm that attempts to find a small connected dominating set in a graph. In this paper we consider the "average...
Jennie C. Hansen, Eric Schmutz, Li Sheng
PODS
2009
ACM
119views Database» more  PODS 2009»
15 years 11 months ago
Exceeding expectations and clustering uncertain data
Database technology is playing an increasingly important role in understanding and solving large-scale and complex scientific and societal problems and phenomena, for instance, un...
Sudipto Guha, Kamesh Munagala
NIPS
1998
15 years 17 days ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis