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NECO
2007
129views more  NECO 2007»
13 years 7 months ago
Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance
It is well-known that, in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its ac...
Shinichi Nakajima, Sumio Watanabe
DATE
2010
IEEE
171views Hardware» more  DATE 2010»
14 years 23 days ago
Digital statistical analysis using VHDL
—Variations of process parameters have an important impact on reliability and yield in deep sub micron IC technologies. One methodology to estimate the influence of these effects...
Manfred Dietrich, Uwe Eichler, Joachim Haase
KDD
2010
ACM
250views Data Mining» more  KDD 2010»
13 years 11 months ago
Modeling relational events via latent classes
Many social networks can be characterized by a sequence of dyadic interactions between individuals. Techniques for analyzing such events are of increasing interest. In this paper,...
Christopher DuBois, Padhraic Smyth
IJCAI
2001
13 years 9 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
UAI
1996
13 years 9 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