Thuy T. Pham

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Springer Theses
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-07518-7
• Softcover reprint of the original 1st ed. 2019
• Seitenzahl: 124
• Erscheinungstermin: 25. Januar 2019
• Englisch
• Abmessung: 235mm x 155mm x 8mm
• Gewicht: 203g
• ISBN-13: 9783030075187
• ISBN-10: 3030075184
• Artikelnr.: 57384045

Inhaltsangabe

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 .