| Description |
1 online resource (xv, 107 pages) : illustrations (some color) |
| Series |
Springer theses, 2190-5053 |
|
Springer theses, 2190-5053
|
| 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 |
| Summary |
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 |
| Notes |
"Doctoral thesis accepted by the University of Sydney, Australia." |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed August 28, 2018) |
| Subject |
Machine learning.
|
|
Artificial intelligence -- Medical applications.
|
|
Medical informatics.
|
|
Data mining.
|
|
Artificial intelligence.
|
|
Molecular biology.
|
|
Biomedical engineering.
|
|
COMPUTERS -- General.
|
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Artificial intelligence -- Medical applications
|
|
Machine learning
|
|
Medical informatics
|
| Form |
Electronic book
|
| ISBN |
9783319986753 |
|
3319986759 |
|
3319986740 |
|
9783319986746 |
|
9783319986760 |
|
3319986767 |
|
9783030075187 |
|
3030075184 |
|
