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IJON
1998
158views more  IJON 1998»
13 years 7 months ago
Bayesian Kullback Ying-Yang dependence reduction theory
Bayesian Kullback Ying—Yang dependence reduction system and theory is presented. Via stochastic approximation, implementable algorithms and criteria are given for parameter lear...
Lei Xu
NN
1998
Springer
177views Neural Networks» more  NN 1998»
13 years 7 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
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
JAIR
2008
93views more  JAIR 2008»
13 years 7 months ago
A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...
TCOM
2010
67views more  TCOM 2010»
13 years 5 months ago
Receive antenna selection for unitary space-time modulation over semi-correlated Ricean channels
—Receive antenna selection for unitary space-time modulation (USTM) over semi-correlated Ricean fading channels is analyzed (this work generalizes that of Ma and Tepedelenlio˘gl...
Mahdi Ramezani, Mahdi Hajiaghayi, Chintha Tellambu...