Hiromasa Kaneko

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 11

Top Topics

Regression Methods

Ensemble Learning

Nonlinear Regression

Top Venues

J. Chem. Inf. Model.

Comput. Chem. Eng.

J. Comput. Aided Mol. Des.

Publications

Kohei Motojima, Abhijit Sen, Yoichi M. A. Yamada, Hiromasa Kaneko
Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions. J. Chem. Inf. Model. 63 (18) (2023)
Kohei Nemoto, Hiromasa Kaneko
De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. J. Chem. Inf. Model. 63 (3) (2023)
Hiromasa Kaneko
Direct prediction of the batch time and process variable profiles using batch process data based on different batch times. Comput. Chem. Eng. 169 (2023)
Shunsuke Yuyama, Hiromasa Kaneko
Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks. J. Chem. Inf. Model. 61 (12) (2021)
Hiromasa Kaneko
Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels. J. Chem. Inf. Model. 58 (2) (2018)
Hiromasa Kaneko
Sparse Generative Topographic Mapping for Both Data Visualization and Clustering. J. Chem. Inf. Model. 58 (12) (2018)
Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu
Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring. Comput. Chem. Eng. 113 (2018)
Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu
On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification. Comput. Chem. Eng. 98 (2017)
Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu
to x). J. Chem. Inf. Model. 56 (2) (2016)
Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu
Ring system-based chemical graph generation for de novo molecular design. J. Comput. Aided Mol. Des. 30 (5) (2016)
Shunichi Takeda, Hiromasa Kaneko, Kimito Funatsu
Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules. J. Chem. Inf. Model. 56 (10) (2016)
Hiromasa Kaneko, Kimito Funatsu
Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses. J. Chem. Inf. Model. 54 (9) (2014)
Hiromasa Kaneko, Kimito Funatsu
Automatic Database Monitoring for Process Control Systems. IEA/AIE (1) (2014)
Hiromasa Kaneko, Kimito Funatsu
Adaptive Soft Sensor Model Using Online Support Vector Regression with the Time Variable and Discussion on Appropriate Parameter Settings. KES (2013)
Hiromasa Kaneko, Kimito Funatsu
Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size. Comput. Chem. Eng. 58 (2013)
Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu
Erratum for "Development of a New Regression Analysis Method Using Independent Component Analysis". J. Chem. Inf. Model. 53 (11) (2013)
Hiromasa Kaneko, Kimito Funatsu
Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation. J. Chem. Inf. Model. 53 (9) (2013)
Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu
Novel soft sensor method for detecting completion of transition in industrial polymer processes. Comput. Chem. Eng. 35 (6) (2011)
Hiromasa Kaneko, Kimito Funatsu
Improvement and Estimation of Prediction Accuracy of Soft Sensor Models Based on Time Difference. IEA/AIE (1) (2011)
Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu
A novel soft sensor method detecting completion of transition for industrial polymer processes. ICONS (2009)
Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu
Development of a New Regression Analysis Method Using Independent Component Analysis. J. Chem. Inf. Model. 48 (3) (2008)