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IEEE Transactions on Neural Networks and Learning Systems|May 9, 2014
Learning with kernel smoothing models and low-discrepancy samplingCristiano Cervellera, Danilo MacciòIEEE Transactions on Neural Networks and Learning Systems|May 13, 2015
Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning MachinesCristiano Cervellera, Danilo MacciòIEEE Transactions on Neural Networks and Learning Systems|October 21, 2014
Local linear regression for function learning: an analysis based on sample discrepancyCristiano Cervellera, Danilo MacciòNeural Networks : the Official Journal of the International Neural Network Society|April 21, 2010
Efficient global maximum likelihood estimation through kernel methodsCristiano Cervellera, Danilo Macciò, Marco MuselliIEEE Transactions on Neural Networks|August 15, 2008
Deterministic learning for maximum-likelihood estimation through neural networksCristiano Cervellera, Danilo Macciò, Marco MuselliIEEE Transactions on Neural Networks|February 23, 2010
Lattice point sets for deterministic learning and approximate optimization problemsCristiano CervelleraIEEE Transactions on Cybernetics|August 5, 2015
F -Discrepancy for Efficient Sampling in Approximate Dynamic ProgrammingCristiano Cervellera, Danilo MaccioIEEE Transactions on Cybernetics|January 20, 2017
An Extreme Learning Machine Approach to Density Estimation ProblemsCristiano Cervellera, Danilo MaccioIEEE Transactions on Neural Networks|September 24, 2004
Deterministic design for neural network learning: an approach based on discrepancyCristiano Cervellera, Marco MuselliIEEE Transactions on Neural Networks and Learning Systems|June 15, 2017
Distribution-Preserving Stratified Sampling for Learning ProblemsCristiano Cervellera, Danilo MaccioPageof 2