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» Maximum likelihood of phylogenetic networks
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ICML
2004
IEEE
16 years 17 days ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
TSP
2012
13 years 7 months ago
Distributed Covariance Estimation in Gaussian Graphical Models
—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient d...
Ami Wiesel, Alfred O. Hero
102
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RECOMB
2005
Springer
16 years 2 days ago
Information Theoretic Approaches to Whole Genome Phylogenies
We describe a novel method for efficient reconstruction of phylogenetic trees, based on sequences of whole genomes or proteomes, whose lengths may greatly vary. The core of our me...
David Burstein, Igor Ulitsky, Tamir Tuller, Benny ...
SODA
2001
ACM
79views Algorithms» more  SODA 2001»
15 years 1 months ago
Learning Markov networks: maximum bounded tree-width graphs
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
David R. Karger, Nathan Srebro
NIPS
1998
15 years 1 months ago
Divisive Normalization, Line Attractor Networks and Ideal Observers
Gain control by divisive inhibition, a.k.a. divisive normalization, has been proposed to be a general mechanism throughout the visual cortex. We explore in this study the statisti...
Sophie Deneve, Alexandre Pouget, Peter E. Latham