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Applying machine learning for automa...
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Pham, Thuy T.
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 settingsby Thuy T. Pham.
Author:
Pham, Thuy T.
Published:
Cham :Springer International Publishing :2019.
Description:
xv, 107 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Machine learning.
Online resource:
http://dx.doi.org/10.1007/978-3-319-98675-3
ISBN:
9783319986753$q(electronic bk.)
Applying machine learning for automated classification of biomedical data in subject-independent settings
Pham, Thuy T.
Applying machine learning for automated classification of biomedical data in subject-independent settings
[electronic resource] /by Thuy T. Pham. - Cham :Springer International Publishing :2019. - xv, 107 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..
Introduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion.
This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
ISBN: 9783319986753$q(electronic bk.)
Standard No.: 10.1007/978-3-319-98675-3doiSubjects--Topical Terms:
188639
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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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.
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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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EB Q325.5 P534 2019 2019
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http://dx.doi.org/10.1007/978-3-319-98675-3
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