Earticle

초록

영어

Gaussian process regression (GPR) is a nonparametric Bayesian methodology that is applied in various places in machine learning. GPR can identify uncertainty by learning data, predicting well, and obtaining variance in prediction. We conducted a study to predict and verify characteristics using a design parameter of a semiconductor using this GPR. In addition, by predicting the characteristic value of the secondary semiconductor derived from the predicted characteristics, it is possible to confirm the characteristics of the generated semiconductor.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Gaussian process regression
III. DESIGN & IMPLEMENTATION
A. Datasets
B. Experiment result R2
C. Experiment result error
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Suhan Son [ School of Electrical Engineering Korea University ]
• Junhee Seok [ School of Electrical Engineering Korea University ] Corresponding Author

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