원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 7th International Conference on Next Generation Computing 2021
2021.11
pp.238-239
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보
