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Kernel techniques: From machine learning to meshless methods

Published online by Cambridge University Press:  16 May 2006

Robert Schaback  and
Holger Wendland
Robert Schaback
Affiliation:
Institut für Numerische und Angewandte Mathematik, Universität Göttingen, Lotzestraβe 16–18, D-37083 Göttingen, Germany E-mail: schaback@math.uni-goettingen.dewendland@math.uni-goettingen.dehttp://www.num.math.uni-goettingen.de/schabackhttp://www.num.math.uni-goettingen.de/wendland
Holger Wendland
Affiliation:
Institut für Numerische und Angewandte Mathematik, Universität Göttingen, Lotzestraβe 16–18, D-37083 Göttingen, Germany E-mail: schaback@math.uni-goettingen.dewendland@math.uni-goettingen.dehttp://www.num.math.uni-goettingen.de/schabackhttp://www.num.math.uni-goettingen.de/wendland
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Abstract

Kernels are valuable tools in various fields of numerical analysis, including approximation, interpolation, meshless methods for solving partial differential equations, neural networks, and machine learning. This contribution explains why and how kernels are applied in these disciplines. It uncovers the links between them, in so far as they are related to kernel techniques. It addresses non-expert readers and focuses on practical guidelines for using kernels in applications.

Type
Research Article
Information
Acta Numerica , Volume 15 , May 2006 , pp. 543 - 639
Copyright
2006 Cambridge University Press

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