Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies

Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies

Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-dri...

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Bibliographic Details
Main Author: Liu, Xiaoyang
Other Authors: Computer Science and Application
Format: Others
Language:en_US
Published: Virginia Tech 2019
Subjects:
Machine learning
Neural Network
Chromatography
Fullerene
Modeling
Random Forest
XGBoost
Linear Regression
SVM regression
Nearest Neighbor
Online Access:http://hdl.handle.net/10919/93737

Internet

http://hdl.handle.net/10919/93737

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