Title: MGWHD-SVM: maximum weighted heteroscedastic migration learning algorithm

Authors: Min Zhang; Lianguang Mo

Addresses: Department of Mechanical and Electronic Engineering, Shandong Management University, Jinan 250357, China ' School of Management, Hunan City University, Yi Yang 413000, China

Abstract: Maximum mean discrepancy (MMD) is a global measure of the distribution differences between domains at present, as a standard for effectively measuring the distribution differences between source and destination domains, however, MMD has some shortcomings in measuring the local structure and distribution differences between fields. This paper proposes a new measure: maximum local weighted heteroscedasticity discrepancy (MLWHD), this measure not only fully considers the local structure and distribution differences among fields, but also shows good adaptability to the exception points and noise, further, MLWHD was used to determine the maximum global weighted heteroscedasticity discrepancy (MGWHD), and MGWHD was embedded into the training of support vector machine (SVM). Finally, the test shows that the MGWHD method has better robustness.

Keywords: MLWHD; support vector machine; SVM; migration learning.

DOI: 10.1504/IJCSM.2021.10041740

International Journal of Computing Science and Mathematics, 2021 Vol.14 No.1, pp.89 - 106

Received: 03 Jan 2019
Accepted: 11 Feb 2019

Published online: 12 Oct 2021 *

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