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» On Generalization by Neural Networks
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ESANN
2003
15 years 5 months ago
Fast approximation of the bootstrap for model selection
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main dif...
Geoffroy Simon, Amaury Lendasse, Vincent Wertz, Mi...
AUSAI
2006
Springer
15 years 7 months ago
Polynomial Pipelined Neural Network and Its Application to Financial Time Series Prediction
A novel type of higher order pipelined neural network, the polynomial pipelined neural network, is presented. The network is constructed from a number of higher order neural networ...
Abir Jaafar Hussain, Adam Knowles, Paulo J. G. Lis...
ITNG
2010
IEEE
15 years 2 months ago
Scalable Intrusion Detection with Recurrent Neural Networks
The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one-size-fits-all...
Longy O. Anyanwu, Jared Keengwe, Gladys A. Arome
JMLR
2008
188views more  JMLR 2008»
15 years 3 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
124
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IJCNN
2000
IEEE
15 years 8 months ago
Sensitivity Analysis for Conic Section Function Neural Networks
Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. After training a neural network, one may want to k...
Lale Özyilmaz, Tülay Yildirim