Abstract
This paper deals with a way of constructing reproducing kernel Hilbert spaces and their associated kernels from frame theory. After introducing briefly frame theory, we give mild conditions on frame elements for spanning a RKHS. Examples of different kernels are then given based on wavelet frame. Thus, issues of this way of building kernel for semiparametric learning are discussed and an application example on a toy problem is described. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Rakotomamonjy, A., & Canu, S. (2002). Frame kernels for learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 707–712). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_115
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