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» Approximate algorithms for neural-Bayesian approaches
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IJCV
2008
188views more  IJCV 2008»
14 years 9 months ago
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequen...
Elise Arnaud, Étienne Mémin
EOR
2006
73views more  EOR 2006»
14 years 9 months ago
Path relinking and GRG for artificial neural networks
Artificial neural networks (ANN) have been widely used for both classification and prediction. This paper is focused on the prediction problem in which an unknown function is appr...
Abdellah El-Fallahi, Rafael Martí, Leon S. ...
FOCM
2006
97views more  FOCM 2006»
14 years 9 months ago
Learning Rates of Least-Square Regularized Regression
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
JMLR
2008
209views more  JMLR 2008»
14 years 9 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
PR
2008
123views more  PR 2008»
14 years 9 months ago
Extensions of vector quantization for incremental clustering
In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental...
Edwin Lughofer