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UID:5567@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20140214T140000
DTEND;TZID=Europe/Paris:20140214T150000
DTSTAMP:20241030T215619Z
URL:https://www.i2m.univ-amu.fr/evenements/j-audiffren-lif-uniform-stabili
 ty-and-consistency-for-operator-valued-kernel-machines/
SUMMARY: (...): J. Audiffren (LIF): Uniform stability and consistency for O
 perator valued kernel machines
DESCRIPTION:: Operator-valued kernels\\\, such as multi-task kernels\\\, ar
 e appropriate for learning problems with nonscalar outputs like structured
  output prediction\\\, and have interesting properties. For instance\\\, t
 hey are particularly efficient for dealing with functional data\\\, and fo
 r taking into account the structure of the output space. However\\\, they 
 have received little attention from the community\\\, particularly from a 
 theoretical point of vue. We will show that operator-valued kernel regress
 ion algorithms are uniformly stable in the general case of infinite-dimens
 ional output spaces. We then derive under mild assumption on the kernel ge
 neralization bounds of such algorithms\\\, and we show their consistency e
 ven with non Hilbert-Schmidt operator-valued kernels.
CATEGORIES:Séminaire,Signal et Apprentissage
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DTSTART:20131027T020000
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