著書・論文・記事など

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●著書

  1. 金子 弘昌, Pythonで学ぶ実験計画法入門 ベイズ最適化によるデータ解析, 講談社, 2021年6月3日 内容
  2. 金子 弘昌, Pythonで気軽に化学・化学工学, 丸善出版, 2021年5月1日 内容
  3. 金子 弘昌, 化学のための Pythonによるデータ解析・機械学習入門, オーム社, 2019年10月23日 内容

●論文

  1. Hiromasa Kaneko, Examining Variable Selection Methods for the Predictive Performance of Regression Models and the Proportion of Selected Variables and Selected Random Variables, Heliyon, 7(6), e07356, 2021. 内容 論文URL
  2. Hiromasa Kaneko, Estimating the Reliability of Predictions in Locally Weighted Partial Least-Squares Modeling, Journal of Chemometrics, in press. 内容 論文URL
  3. Hiromasa Kaneko, Extended Gaussian Mixture Regression for Forward and Inverse Analysis, Chemometrics and Intelligent Laboratory Systems, 213, 104325, 2021. 内容 論文URL
  4. Ryo Iwama, Hiromasa Kaneko, Design of Ethylene Oxide Production Process Based on Adaptive Design of Experiments and Bayesian Optimization, Journal of Advanced Manufacturing and Processing, 3(3), e10085, 2021. 内容 論文URL
  5. Hiromasa Kaneko, Shunsuke Kono, Akihiro Nojima, Takuya Kambayashi, Transfer Learning and Wavelength Selection Method in NIR Spectroscopy to Predict Glucose and Lactate Concentrations in Culture Media Using VIP-Boruta, Analytical Science Advances, in press. 内容 論文URL
  6. Kaina Shibata, Hiromasa Kaneko, Prediction of Spin–Spin Coupling Constants with Machine Learning in NMR, Analytical Science Advances, in press. 内容 論文URL
  7. Hiromasa Kaneko, Estimation and Visualization of Process States Using Latent Variable Models Based on Gaussian Process, Analytical Science Advances, 2(5-6), 326-333, 2021. 内容 論文URL
  8. Hiromasa Kaneko, Adaptive Design of Experiments Based on Gaussian Mixture Regression, Chemometrics and Intelligent Laboratory Systems, 208, 104226, 2021. 内容 論文URL
  9. Hiromasa Kaneko, Support Vector Regression That Takes into Consideration the Importance of Explanatory Variables, Journal of Chemometrics, 35(4), e3327, 2021. 内容 論文URL
  10. Hiroki Yoshihama, Hiromasa Kaneko, Design of Thermoelectric Materials with High Electrical Conductivity, High Seebeck Coefficient, and Low Thermal Conductivity, Analytical Science Advances, 2(5-6), 289-294, 2021. 内容 論文URL
  11. Naoto Shimizu, Hiromasa Kaneko, Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests, Journal of Computer Chemistry, Japan, accepted. 内容 論文URL
  12. Fumika Nitta, Hiromasa Kaneko, Two‐ and Three‐Dimensional Quantitative Structure‐Activity Relationship Models Based on Conformer Structures, Molecular Informatics, 40(3), 2000123, 2021. 内容 論文URL
  13. Naoto Shimizu, Hiromasa Kaneko, Direct Inverse Analysis Based on Gaussian Mixture Regression for Multiple Objective Variables in Material Design, Materials & Design, 196, 109168, 2020. 内容 論文URL
  14. Yasuhiro Kanno, Hiromasa Kaneko, Ensemble Just-in-time Model Based on Gaussian Process Dynamical Models for Nonlinear and Dynamic Processes, Chemometrics and Intelligent Laboratory Systems, 203, 104061, 2020. 内容 論文URL
  15. Kazukuni Tahara, Yuki Kubo, Shingo Hashimoto, Toru Ishikawa, Hiromasa Kaneko, Anton Brown, Brandon Hirsch, Steven De Feyter, Yoshito Tobe, Porous Self-Assembled Molecular Networks as Templates for Chiral Position-Controlled Chemical Functionalization of Graphitic Surfaces, Journal of the American Chemical Society,142(16), 7699-7708, 2020. 論文URL
  16. 佐藤 圭悟, 金子 弘昌, モデルの適用範囲の考慮したアンサンブル学習法の開発, Journal of Computer Chemistry, Japan, 18(4), 187-193, 2019. 内容 論文URL
  17. 高野 森乃介, 金子 弘昌, 高屈折率および高ガラス転移温度をもつ高分子材料のモノマー設計, Journal of Computer Chemistry, Japan, 18(2), 115-121, 2019. 内容 論文URL
  18. Hiromasa Kaneko, Estimation of Predictive Performance for Test Data in Applicability Domains Using y-randomization, Journal of Chemometrics, 33(9), e3171, 2019. 内容 論文URL
  19. Yasuhiro Kanno, Hiromasa Kaneko, Improvement of Predictive Accuracy in Semi-Supervised Regression Analysis by Selecting Unlabeled Chemical Structures, Chemometrics and Intelligent Laboratory Systems, 191, 82-87, 2019. 内容 論文URL
  20. Hiromasa Kaneko, Beware of r2 even for Test Datasets: Using the Latest Measured y-values (r2LM) in Time Series Data Analysis, Journal of Chemometrics, 33(2), e3093, 2019. 内容 論文URL
  21. Hiromasa Kaneko, Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping, Molecular Informatics, 38(3), 1800088, 2019. 内容 論文URL
  22. Hiromasa Kaneko, Sparse Generative Topographic Mapping for Both Data Visualization and Clustering, Journal of Chemical Information and Modeling, 58(12), 2528-2535, 2018. 内容 論文URL
  23. Hiromasa Kaneko, Illustration of Merits of Semi-supervised Learning in Regression Analysis, Chemometrics and Intelligent Laboratory Systems, 182, 47-56, 2018. 内容 論文URL
  24. Hiromasa Kaneko, Automatic Outlier Sample Detection Based on Regression Analysis and Repeated Ensemble Learning, Chemometrics and Intelligent Laboratory Systems, 177, 74-82, 2018. 内容 論文URL
  25. Hiromasa Kaneko, K-Nearest Neighbor Normalized Error for Visualization and Reconstruction – A New Measure for Data Visualization Performance, Chemometrics and Intelligent Laboratory Systems, 176, 22-33, 2018. 内容 論文URL
  26. Hiromasa Kaneko, Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Sub-Models, Journal of Chemical Information and Modeling, 58(2), 480–489, 2018. 内容 論文URL
  27. Hiromasa Kaneko, A New Measure of Regression Model Accuracy that Considers Applicability Domains, Chemometrics and Intelligent Laboratory Systems, 171, 1-8, 2017. 内容 論文URL

* 金子が明治大学に異動する前の研究成果 (査読付き論文 68 報など) について興味のある方は別途ご連絡ください。

●記事・連載・特集など

  1. 金子弘昌,「誰にも負けない努力」, クシノシルス, 2021年3月2日
  2. 金子弘昌,「常に考え続けたり、現場の声を聞くようにしよう」, Meiji.net, 2020年9月3日
  3. 金子弘昌,「人工知能を作ることから始まる現代のものづくり」, Meiji.net, 2020年8月26日
  4. 金子弘昌,「プログラミング未経験者のためのデータ解析・機械学習」, 化学工学(化学工学会の会誌), 2019年8月(Vol.83 No.8) から 2020年6月(Vol.84 No.6) まで全 12 回連載
  5. 金子弘昌, 「計測できないパラメータを人工知能で計算する『ソフトセンサー技術』に迫る」, minsaku みんなの試作広場, 2018年7月2日

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