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UID:8401@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20140408T140000
DTEND;TZID=Europe/Paris:20140408T150000
DTSTAMP:20241120T210401Z
URL:https://www.i2m.univ-amu.fr/evenements/luminy-a-rakotomamonjy-univ-rou
 en-efficient-optimization-of-the-multiple-kernel-learning-problem/
SUMMARY: (...): [Luminy] A. Rakotomamonjy (Univ. Rouen) : Efficient optimiz
 ation of the multiple kernel learning problem
DESCRIPTION:: Efficient optimization of the multiple kernel learning proble
 m\n\nBy Alain Rakotomamonjy\\\, Univ. Rouen.\n\nKernel methods are widely 
 used to address a variety of learning tasks including classification\\\, r
 egression\\\,  ranking\\\, clustering\\\, and dimensionality reduction. Th
 e choice of a kernel is often left to the user. But poor selections may le
 ad to sub-optimal performance. Instead\\\, the kernel selection process sh
 ould  be included as part of the overall learning problem. In this way\\\,
  better performance guarantees can be given and the kernel selection proce
 ss can be made automatic. Algorithms that are able to address these issues
  are denoted as Multiple Kernel Learning (MKL) algorithm and they have bee
 n now widely used for learning with multiple views of the same data or for
  selecting/fusing different features and kernels.\n\nIn this talk\\\, I wi
 ll review the basics of MKL and discuss two issues\na how can we solve the
  MKL optimization problem when the number of kernels is very large (or inf
 inite) ?\nb how can we solve the MKL optimization problem when only low-ra
 nk approximation of the different kernels are available ?
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DTSTART:20140330T030000
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