Learning With Kernel Smoothing Models and Low-Discrepancy Sampling

This brief presents an analysis of the performance of kernel smoothing models used to estimate an unknown target function, addressing the case where the choice of the training set is part of the learning process. In particular, we consider a choice of the points at which the function is observed bas...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2013-03, Vol.24 (3), p.504-509
Hauptverfasser:	Cervellera, Cristiano, Maccio, Danilo
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Approximation methods Artificial intelligence Computer science control theory systems Context Convergence Data processing. List processing. Character string processing Empirical risk minimization Estimation Exact sciences and technology function learning Kernel kernel smoothing models Learning and adaptive systems low-discrepancy sequences Memory organisation. Data processing Random sequences Smoothing methods Software
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