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Cristiano Cervellera

Showing results (1-10 of 12) with videos related to

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IEEE Transactions on Neural Networks|February 23, 2010
Lattice point sets for deterministic learning and approximate optimization problemsCristiano Cervellera
IEEE Transactions on Cybernetics|August 5, 2015
F -Discrepancy for Efficient Sampling in Approximate Dynamic ProgrammingCristiano Cervellera, Danilo Maccio
IEEE Transactions on Cybernetics|January 20, 2017
An Extreme Learning Machine Approach to Density Estimation ProblemsCristiano Cervellera, Danilo Maccio
IEEE Transactions on Neural Networks and Learning Systems|May 9, 2014
Learning with kernel smoothing models and low-discrepancy samplingCristiano Cervellera, Danilo Macciò
IEEE Transactions on Neural Networks and Learning Systems|October 21, 2014
Local linear regression for function learning: an analysis based on sample discrepancyCristiano Cervellera, Danilo Macciò
IEEE 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 Macciò
IEEE Transactions on Neural Networks|September 24, 2004
Deterministic design for neural network learning: an approach based on discrepancyCristiano Cervellera, Marco Muselli
IEEE Transactions on Neural Networks and Learning Systems|June 15, 2017
Distribution-Preserving Stratified Sampling for Learning ProblemsCristiano Cervellera, Danilo Maccio
IEEE Transactions on Cybernetics|February 11, 2017
A Novel Approach for Sampling in Approximate Dynamic Programming Based on $F$ -DiscrepancyCristiano Cervellera, Danilo Maccio
IEEE Transactions on Neural Networks|February 7, 2007
Design of asymptotic estimators: an approach based on neural networks and nonlinear programmingAngelo Alessandri, Cristiano Cervellera, Marcello Sanguineti
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
IEEE Transactions on Neural Networks|February 23, 2010
Lattice point sets for deterministic learning and approximate optimization problemsCristiano Cervellera
IEEE Transactions on Cybernetics|August 5, 2015
F -Discrepancy for Efficient Sampling in Approximate Dynamic ProgrammingCristiano Cervellera, Danilo Maccio
IEEE Transactions on Cybernetics|January 20, 2017
An Extreme Learning Machine Approach to Density Estimation ProblemsCristiano Cervellera, Danilo Maccio
IEEE Transactions on Neural Networks and Learning Systems|May 9, 2014
Learning with kernel smoothing models and low-discrepancy samplingCristiano Cervellera, Danilo Macciò
IEEE Transactions on Neural Networks and Learning Systems|October 21, 2014
Local linear regression for function learning: an analysis based on sample discrepancyCristiano Cervellera, Danilo Macciò
IEEE 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 Macciò
IEEE Transactions on Neural Networks|September 24, 2004
Deterministic design for neural network learning: an approach based on discrepancyCristiano Cervellera, Marco Muselli
IEEE Transactions on Neural Networks and Learning Systems|June 15, 2017
Distribution-Preserving Stratified Sampling for Learning ProblemsCristiano Cervellera, Danilo Maccio
IEEE Transactions on Cybernetics|February 11, 2017
A Novel Approach for Sampling in Approximate Dynamic Programming Based on $F$ -DiscrepancyCristiano Cervellera, Danilo Maccio
IEEE Transactions on Neural Networks|February 7, 2007
Design of asymptotic estimators: an approach based on neural networks and nonlinear programmingAngelo Alessandri, Cristiano Cervellera, Marcello Sanguineti
Pageof 2