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» Adaptive importance sampling in general mixture classes
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CSDA
2011
13 years 2 months ago
Approximate forward-backward algorithm for a switching linear Gaussian model
Motivated by the application of seismic inversion in the petroleum industry we consider a hidden Markov model with two hidden layers. The bottom layer is a Markov chain and given ...
Hugo Hammer, Håkon Tjelmeland
ESANN
2006
13 years 8 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
GECCO
2003
Springer
153views Optimization» more  GECCO 2003»
14 years 18 days ago
SEPA: Structure Evolution and Parameter Adaptation in Feed-Forward Neural Networks
Abstract. In developing algorithms that dynamically changes the structure and weights of ANN (Artificial Neural Networks), there must be a proper balance between network complexit...
Paulito P. Palmes, Taichi Hayasaka, Shiro Usui
SIBGRAPI
2006
IEEE
14 years 1 months ago
Adapted Dynamic Meshes for Deformable Surfaces
Deformable objects play an important role in many applications, such as animation and simulation. Effective computation with deformable surfaces can be achieved through the use of...
Fernando de Goes, Felipe P. G. Bergo, Alexandre X....
ICA
2007
Springer
14 years 1 months ago
Compressed Sensing and Source Separation
Abstract. Separation of underdetermined mixtures is an important problem in signal processing that has attracted a great deal of attention over the years. Prior knowledge is requir...
Thomas Blumensath, Mike E. Davies