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A Generalized Kernel Approach to Structured Output Learning

Hachem Kadri 1, 2 Mohammad Ghavamzadeh 2 Philippe Preux 2
1 QARMA - éQuipe AppRentissage et MultimediA [Marseille]
LIF - Laboratoire d'informatique Fondamentale de Marseille
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) problem using operator-valued kernels. We show that some of the existing formulations of this problem are special cases of our framework. We then propose a covariance-based operator-valued kernel that allows us to take into account the structure of the kernel feature space. This kernel operates on the output space and encodes the interactions between the outputs without any reference to the input space. To address this issue, we introduce a variant of our KDE method based on the conditional covariance operator that in addition to the correlation between the outputs takes into account the effects of the input variables. Finally, we evaluate the performance of our KDE approach using both covariance and conditional covariance kernels on two structured output problems, and compare it to the state-of-the-art kernel-based structured output regression methods.
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Submitted on : Wednesday, July 15, 2015 - 7:48:38 PM
Last modification on : Thursday, January 20, 2022 - 4:16:29 PM
Long-term archiving on: : Wednesday, April 26, 2017 - 3:38:36 AM


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  • HAL Id : hal-00695631, version 2
  • ARXIV : 1205.2171


Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux. A Generalized Kernel Approach to Structured Output Learning. International Conference on Machine Learning (ICML), Jun 2013, Atlanta, United States. ⟨hal-00695631v2⟩



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