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Lattice Point Sets for Deterministic Learning and Approximate Optimization Problems.

Authors :
Cervellera, Cristiano
Source :
IEEE Transactions on Neural Networks. Apr2010, Vol. 21 Issue 4, p687-692. 6p. 4 Charts, 4 Graphs.
Publication Year :
2010

Abstract

In this brief, the use of lattice point sets (LPSs) is investigated in the context of general learning problems (including function estimation and dynamic optimization), in the case where the classic empirical risk minimization (ERM) principle is considered and there is freedom to choose the sampling points of the input space. Here it is proved that convergence of the ERM principle is guaranteed when LPSs are employed as training sets for the learning procedure, yielding up to a superlinear convergence rate under some regularity hypotheses on the involved functions. Preliminary simulation results are also provided. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10459227
Volume :
21
Issue :
4
Database :
Academic Search Index
Journal :
IEEE Transactions on Neural Networks
Publication Type :
Academic Journal
Accession number :
49138294
Full Text :
https://doi.org/10.1109/TNN.2010.2041360