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Summary: To appear in 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Multiple Kernel Machines
Using Localized Kernels
Mehmet G¨onen and Ethem Alpaydin
Department of Computer Engineering
Bogazi¸ci University
TR-34342, Bebek, Istanbul, Turkey
gonen@boun.edu.tr alpaydin@boun.edu.tr
Abstract. Multiple kernel learning (Mkl) 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 in-
put space. Localized multiple kernel learning (Lmkl) framework extends
the Mkl framework to allow combining kernels with different weights
in different regions of the input space by using a gating model. Lmkl
extracts the relative importance of kernels in each region whereas Mkl
gives their relative importance over the whole input space. In this paper,
we generalize the Lmkl framework with a kernel-based gating model and
derive the learning algorithm for binary classification. Empirical results
on toy classification problems are used to illustrate the algorithm. Ex-
periments on two bioinformatics data sets are performed to show that
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