A new summation multi-kernel learning
method based on sample weighting

Abstract

Abstract:

Multi-kernel learning is a new research hotspot in current kernel machine learning field. By mapping data

into the high dimensional space, kernel methods increase the computing performance of linear classifiers such

as support vector machines, and it is a convenient and effective way to deal with nonlinear pattern

recognition and classification. However, in some complex situations, such as heterogeneous data or irregular

data, large sample size and non-flat sample distribution, the kernel learning method based on single kernel

function cannot completely meet the requirement, so it is necessary to develop multiple kernel functions in

order to get better results. We propose a new summation multi-kernel learning method based on sample

weighting which can be weighted by the capability of how much a single kernel function can fit the sample.

Experiment analysis on several data sets shows that the proposed method can obtain high classification

accuracy.

Key words: multi-kernel learning, map, nonlinear model, heterogeneous data

SHEN Jian，JIANG Yun，ZHANG Ya-nan，HU Xue-wei.

A new summation multi-kernel learning

method based on sample weighting

[J]. Computer Engineering & Science.

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A new summation multi-kernel learning

method based on sample weighting

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