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» Feature Correspondence: A Markov Chain Monte Carlo Approach
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PRL
2000
182views more  PRL 2000»
13 years 7 months ago
Bayesian MLP neural networks for image analysis
We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
Aki Vehtari, Jouko Lampinen
ICPR
2008
IEEE
14 years 1 months ago
Approximation of salient contours in cluttered scenes
This paper proposes a new approach to describe the salient contours in cluttered scenes. No need to do the preprocessing, such as edge detection, we directly use a set of random s...
Rui Huang, Nong Sang, Qiling Tang
EVOW
2008
Springer
13 years 9 months ago
Detection of Quantitative Trait Associated Genes Using Cluster Analysis
Abstract. Many efforts have been involved in association study of quantitative phenotypes and expressed genes. The key issue is how to efficiently identify phenotype-associated gen...
Zhenyu Jia, Sha Tang, Dan Mercola, Shizhong Xu
WSC
1998
13 years 8 months ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng
UAI
1996
13 years 8 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon