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Applying machine learning for automa...

Pham, Thuy T.

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Applying machine learning for automated classification of biomedical data in subject-independent settings

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying machine learning for automated classification of biomedical data in subject-independent settings/ by Thuy T. Pham.
Author:	Pham, Thuy T.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xv, 107 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-98675-3
ISBN:	9783319986753

Applying machine learning for automated classification of biomedical data in subject-independent settings
Pham, Thuy T.

Applying machine learning for automated classification of biomedical data in subject-independent settings[electronic resource] /by Thuy T. Pham. - Cham :Springer International Publishing :2019. - xv, 107 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

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.

ISBN: 9783319986753

Standard No.: 10.1007/978-3-319-98675-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Applying machine learning for automated classification of biomedical data in subject-independent settings
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245 1 0 $a Applying machine learning for automated classification of biomedical data in subject-independent settings $h [electronic resource] / $c by Thuy T. Pham.
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505 0 $a 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.
520 $a 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.
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