Multiple graph kernel learning based on GMDH-type neural network

Lixiang Xu

doi:10.1016/j.inffus.2020.08.025

Return

Multiple graph kernel learning based on GMDH-type neural network

2021-02-01

PRE

王晓峰 (Xiaofeng Wang) *

Yuanyan Tang

DOI：10.1016/j.inffus.2020.08.025

Original

Original request for help

Save

Abstract

En 中文

Multiple kernel learning (MKL), as a principled classification method, selects and combines base kernels to increase the categorization accuracy of Support Vector Machines (SVMs). The group method of data handling neural network (GMDH-NN) has been applied in many fields of optimization, data mining, and pattern recognition. It can automatically seek interrelatedness in data, select an optimal structure for the model or network, and enhance the accuracy of existing algorithms. We can utilize the advantages of the GMDH-NN to build a multiple graph kernel learning (MGKL) method and enhance the categorization performance of graph kernel SVMs. In this paper, we first define a unitized symmetric regularity criterion (USRC) to improve the symmetric regularity criterion of GMDH-NN. Second, a novel structure for the initial model of the GMDH-NN is defined, which uses the posterior probability output of graph kernel SVMs. We then use a hybrid graph kernel in the H-1-space for MGKL in combination with the GMDH-NN. This way, we can obtain a pool of optimal graph kernels with different kernel parameters. Our experiments on standard graph datasets show that this new MGKL method is highly effective.

Keywords:

Support vector machine

Group method of data handling

Ensemble selection

Regularity criterion

Probabilistic output

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.