@inproceedings{6c8e09376f5344f599fbe7d1c930f9e9,
title = "Effect of Sampling Method on the Regression Accuracy for a High-Speed Link Problem",
abstract = "We examine the effect of different sampling methods on the accuracy of support vector regression models for eye opening prediction of a high-speed link. Four different sampling methods are tested to generate training data for training the machine learning model for the equalizers on a high-speed link problem, and the accuracy of the models to predict eye opening is compared. Latin Hypercube shows superior performance in terms of mean square error and R2 compared to other methods tested.",
keywords = "Latin Hypercube, Machine learning, eye diagram, high-speed link, sparse grid, support vector regression",
author = "Shangguan, {Xing Jian} and Hanzhi Ma and Cangellaris, {Andreas C.} and Xu Chen",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021 ; Conference date: 17-10-2021 Through 20-10-2021",
year = "2021",
doi = "10.1109/EPEPS51341.2021.9609130",
language = "English (US)",
series = "EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "EPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems",
address = "United States",
}