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13

ESANN
2007

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Complexity bounds of radial basis functions and multi-objective learning

13 years 5 months ago

Complexity bounds of radial basis functions and multi-objective learning

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In the paper, the problem of multi-objective (MOBJ) learning is discussed. The problem of obtaining apparent (eﬀective) complexity measure, which is one of the objectives, is considered. For the speciﬁc case of RBFN, the bounds on the smoothness-based complexity measure are proposed. As shown in the experimental part, the bounds can be used for Pareto set approximation.

Illya Kokshenev, Antônio de Pádua Bra

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Complexity Measure | ESANN 2007 | Neural Networks | Pareto Set Approximation | Smoothness-based Complexity Measure |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2007
Where	ESANN
Authors	Illya Kokshenev, Antônio de Pádua Braga

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