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Localized Multiple Kernel Learning Mehmet Gonen gonen@boun.edu.tr
 

Summary: Localized Multiple Kernel Learning
Mehmet G¨onen gonen@boun.edu.tr
Ethem Alpaydin alpaydin@boun.edu.tr
Department of Computer Engineering, Bogazi¸ci University, TR-34342, Bebek, Istanbul, Turkey
Abstract
Recently, instead of selecting a single kernel,
multiple kernel learning (MKL) has been pro-
posed which uses a convex combination of
kernels, where the weight of each kernel is
optimized during training. However, MKL
assigns the same weight to a kernel over the
whole input space. In this paper, we develop
a localized multiple kernel learning (LMKL)
algorithm using a gating model for select-
ing the appropriate kernel function locally.
The localizing gating model and the kernel-
based classifier are coupled and their opti-
mization is done in a joint manner. Empiri-
cal results on ten benchmark and two bioin-
formatics data sets validate the applicability

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences