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» Covariance Kernels from Bayesian Generative Models
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UAI
2003
13 years 9 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
CGF
2004
93views more  CGF 2004»
13 years 7 months ago
Prototype Modeling from Sketched Silhouettes based on Convolution Surfaces
This paper presents a hybrid method for creating three-dimensional shapes by sketching silhouette curves. Given a silhouette curve, we approximate its medial axis as a set of line...
Chiew-Lan Tai, Hongxin Zhang, Jacky Chun-Kin Fong
BMCBI
2011
13 years 2 months ago
Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression
Background: When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be ...
John J. Heine, Walker H. Land Jr., Kathleen M. Ega...
DIS
2007
Springer
14 years 1 months ago
A Hilbert Space Embedding for Distributions
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reprodu...
Alexander J. Smola, Arthur Gretton, Le Song, Bernh...
ICASSP
2008
IEEE
14 years 2 months ago
A multi-class MLLR kernel for SVM speaker recognition
Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM)...
Zahi N. Karam, William M. Campbell