@article{oai:repo.qst.go.jp:00074767,
 author = {上野, 哲朗 and 日野, 英逸 and 小野, 寛太 and Ueno, Tetsuro},
 issue = {3},
 journal = {表面と真空},
 month = {Mar},
 note = {We present an adaptive design of experiment (DoE) by machine learning for X-ray spectroscopy to improve its efficiency. One of the machine learning techniques, Gaussian process regression predicts a spectrum from the experimental data and determines the optimal energy points to measure. Adaptive DoE successfully reduces total energy points to measure as compared to an X-ray magnetic circular dichroism spectroscopy experiment by a conventional DoE. This method has potential applicability to various measurements and reduces the time and cost of experiments.},
 pages = {147--152},
 title = {機械学習によるX線スペクトル計測の効率化},
 volume = {62},
 year = {2019}
}