Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
(Sprache: Englisch)
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...
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Klappentext zu „Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings “
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.
Inhaltsverzeichnis zu „Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings “
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 .Bibliographische Angaben
- Autor: Thuy T. Pham
- 2018, 1st ed. 2019, XV, 107 Seiten, 32 farbige Abbildungen, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319986740
- ISBN-13: 9783319986746
- Erscheinungsdatum: 31.08.2018
Sprache:
Englisch
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