Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings / by Thuy T. Pham

Von:

Pham, Thuy T [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer Theses, Recognizing Outstanding Ph.D. Research | SpringerLink BücherVerlag: Cham : Springer International Publishing, 2019Beschreibung: Online-Ressource (XV, 107 p. 35 illus., 32 illus. in color, online resource)ISBN:

9783319986753

Schlagwörter:

Andere physische Formen: 9783319986746 | 9783319986760 | Erscheint auch als: 978-3-319-98674-6 Druck-Ausgabe | Printed edition: 9783319986746 | Printed edition: 9783319986760 DDC-Klassifikation:

610.28

LOC-Klassifikation:

R856-857

DOI: DOI: 10.1007/978-3-319-98675-3Online-Ressourcen:

Zusammenfassung: 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 settingsZusammenfassung: 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 -- ConclusionPPN: PPN: 1030109206Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE

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