Authors: Hideitsu Hino
Publish Date: 2014/06/11
Volume: 79, Issue: 2, Pages: 201-210
Abstract
Kernel methods have become a standard solution for a large number of data analyses and extensively utilized in the field of signal processing including analysis of speech image time series and DNA sequences The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data and multiple kernel learning MKL is one of the principled approaches for kernel design problem In this paper a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning and in realizing an MKL algorithm which only optimizes the kernel combination coefficients while the conventional methods need optimizing both combination coefficients and classifier parameters The proposed kernel learning approach is shown to have good classification accuracy through a set of standard datasets for dichotomy classification
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