Applying machine learning for automated classification of biomedical data in subject-independent settings : doctoral thesis accepted by the University of Sydney, Australia
Author(s)
Bibliographic Information
Applying machine learning for automated classification of biomedical data in subject-independent settings : doctoral thesis accepted by the University of Sydney, Australia
（Springer theses : recognizing outstanding Ph. D. research）
Springer, c2019
- : softcover
Available at 1 libraries
Note
"Softcover re-print of the hardcover 1st edition 2019"--T.p. verso
Includes bibliographical references
Description and Table of Contents
Description
This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
Table of Contents
Introduction .- Background .- Algorithms .- Point Anomaly Detection: Application to Freezing of Gait Monitoring .- Collective Anomaly Detection: Application to Respiratory Artefact Removals.- Spike Sorting: Application to Motor Unit Action Potential Discrimination .- Conclusion .
by "Nielsen BookData"