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» Modeling spiking neural networks
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ESANN
2000
15 years 1 months ago
Parametric approach to blind deconvolution of nonlinear channels
A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that ...
Jordi Solé i Casals, Anisse Taleb, Christia...
NIPS
1998
15 years 1 months ago
Controlling the Complexity of HMM Systems by Regularization
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the E...
Christoph Neukirchen, Gerhard Rigoll
BMCBI
2010
103views more  BMCBI 2010»
14 years 12 months ago
Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information
Background: Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the...
Jagat S. Chauhan, Nitish K. Mishra, Gajendra P. S....
JMLR
2006
104views more  JMLR 2006»
14 years 11 months ago
Learning Image Components for Object Recognition
In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or fea...
Michael W. Spratling
COMCOM
2002
189views more  COMCOM 2002»
14 years 11 months ago
Intrusion detection techniques and approaches
Recent security incidents and analysis have demonstrated that manual response to such attacks is no longer feasible. Intrusion Detection systems offer techniques for modelling and...
Theuns Verwoerd, Ray Hunt