Hiromasa Kaneko

发表

New Evaluation Method of Soft Sensors Considering Characteristics of Time Series Data

Takumi Kojima, Hiromasa Kaneko, 2019 .

Practical Use of Savitzky-Golay Filtering-Based Ensemble Online SVR

Kimito Funatsu, Hiromasa Kaneko, Takuya Matsumoto, 2016 .

k-nearest neighbor normalized error for visualization and reconstruction – A new measure for data visualization performance

Hiromasa Kaneko, H. Kaneko, 2018 .

Automatic outlier sample detection based on regression analysis and repeated ensemble learning

Hiromasa Kaneko, 2018 .

Nonlinear Dynamic Feature Extraction Based on Gaussian Process Dynamical Models for Jit-Based Adaptive Soft Sensors

Hiromasa Kaneko, Yasuhiro Kanno, 2019 .

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels

Hiromasa Kaneko, 2018, J. Chem. Inf. Model..

Illustration of merits of semi-supervised learning in regression analysis

Hiromasa Kaneko, H. Kaneko, 2018, Chemometrics and Intelligent Laboratory Systems.

Selective Use of Adaptive Models Considering the Prediction Efficiencies

Kimito Funatsu, Hiromasa Kaneko, Kenichi Tanaka, 2018 .

Development of Ensemble Learning Method Considering Applicability Domains

Hiromasa Kaneko, Keigo Sato, 2019 .

Sparse Generative Topographic Mapping for Both Data Visualization and Clustering

Hiromasa Kaneko, H. Kaneko, 2018, J. Chem. Inf. Model..

Improvement of Prediction Accuracy in Just-In-Time Modelling Using Distance-based Database Update

Kimito Funatsu, Hiromasa Kaneko, Kenichi Tanaka, 2015 .

Estimation of predictive performance for test data in applicability domains using y‐randomization

Hiromasa Kaneko, H. Kaneko, 2019, Journal of Chemometrics.

Constructing Interpretable and Accurate Model Combining Decision Tree and Random Forest

Hiromasa Kaneko, Naoto Shimizu, 2019 .