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TSP
2010
15 years 1 months ago
Learning Gaussian tree models: analysis of error exponents and extremal structures
The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure a...
Vincent Y. F. Tan, Animashree Anandkumar, Alan S. ...
SPEECH
2008
81views more  SPEECH 2008»
15 years 6 months ago
Supervised and unsupervised learning of multidimensionally varying non-native speech categories
The acquisition of novel phonetic categories is hypothesized to be affected by the distributional properties of the input, the relation of the new categories to the native phonolo...
Martijn Goudbeek, Anne Cutler, Roel Smits
IJAR
2010
130views more  IJAR 2010»
15 years 5 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki
ICGI
2010
Springer
15 years 4 months ago
Learning Context Free Grammars with the Syntactic Concept Lattice
The Syntactic Concept Lattice is a residuated lattice based on the distributional structure of a language; the natural representation based on this is a context sensitive formalism...
Alexander Clark
ICCV
2001
IEEE
16 years 9 months ago
Learning Inhomogeneous Gibbs Model of Faces by Minimax Entropy
In this paper we propose a novel inhomogeneous Gibbs model by the minimax entropy principle, and apply it to face modeling. The maximum entropy principle generalizes the statistic...
Ce Liu, Song Chun Zhu, Heung-Yeung Shum