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CVPR
2000
IEEE
14 years 10 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
CSB
2002
IEEE
169views Bioinformatics» more  CSB 2002»
14 years 1 months ago
Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...
IJCAI
1989
13 years 10 months ago
A Critique of the Valiant Model
This paper considers the Valiant framework as it is applied to the task of learning logical concepts from random examples. It is argued that the current interpretation of this Val...
Wray L. Buntine
EOR
2006
68views more  EOR 2006»
13 years 8 months ago
Modelling complex assemblies as a queueing network for lead time control
In this paper we develop an open queueing network for optimal design of multi-stage assemblies, in which each service station represents a manufacturing or assembly operation. The...
Amir Azaron, Hideki Katagiri, Kosuke Kato, Masatos...
ECML
2001
Springer
14 years 1 months ago
Learning of Variability for Invariant Statistical Pattern Recognition
In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class speciï...
Daniel Keysers, Wolfgang Macherey, Jörg Dahme...