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» Graph model selection using maximum likelihood
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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
14 years 2 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
ACL
2006
13 years 9 months ago
An All-Subtrees Approach to Unsupervised Parsing
We investigate generalizations of the allsubtrees "DOP" approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences ...
Rens Bod
ICC
2007
IEEE
104views Communications» more  ICC 2007»
14 years 1 months ago
A Novel Graph Model for Maximum Survivability in Mesh Networks under Multiple Generic Risks
— This paper investigates the path protection problem in mesh networks under multiple generic risks. Disjoint logical links may fail simultaneously if they share the same compone...
Qingya She, Xiaodong Huang, Jason P. Jue
AVBPA
2005
Springer
225views Biometrics» more  AVBPA 2005»
13 years 9 months ago
Video-Based Face Recognition Using Bayesian Inference Model
There has been a flurry of works on video sequence-based face recognition in recent years. One of the hard problems in this area is how to effectively combine the facial configu...
Wei Fan, Yunhong Wang, Tieniu Tan
TSP
2008
102views more  TSP 2008»
13 years 7 months ago
Spatially Adaptive Estimation via Fitted Local Likelihood Techniques
Abstract--This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. T...
Vladimir Katkovnik, Vladimir Spokoiny