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» Evolving Multilayer Perceptrons
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IJON
2010
109views more  IJON 2010»
14 years 6 months ago
Variational inference for Student-t MLP models
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Hang T. Nguyen, Ian T. Nabney
TNN
2008
96views more  TNN 2008»
14 years 11 months ago
Global Convergence and Limit Cycle Behavior of Weights of Perceptron
In this paper, it is found that the weights of a perceptron are bounded for all initial weights if there exists a nonempty set of initial weights that the weights of the perceptron...
Charlotte Yuk-Fan Ho, Bingo Wing-Kuen Ling, Hak-Ke...
ICASSP
2009
IEEE
15 years 6 months ago
A split quaternion nonlinear adaptive filter
A split quaternion learning algorithm for the training of nonlinear finite impulse response filters for the modelling of hypercomplex signals is proposed. A rigorous derivation ...
Bukhari Che Ujang, Clive Cheong Took, Alek Kavcic,...
ICASSP
2008
IEEE
15 years 6 months ago
Ratio semi-definite classifiers
We present a novel classification model that is formulated as a ratio of semi-definite polynomials. We derive an efficient learning algorithm for this classifier, and apply it...
Jonathan Malkin, Jeff Bilmes
IBPRIA
2003
Springer
15 years 4 months ago
A Probabilistic Model for the Cooperative Modular Neural Network
Abstract. This paper presents a model for the probability of correct classification for the Cooperative Modular Neural Network (CMNN). The model enables the estimation of the perf...
Luís A. Alexandre, Aurélio C. Campil...