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 by Thuy T. Pham - 1 recurso en línea (XV, 107 páginas) 35 ilustraciones, 32 ilustraciones a color - Engineering (Springer-11647) Springer Theses Recognizing Outstanding Ph.D. Research 2190-5053 .
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.
9783319986753
10.1007/978-3-319-98675-3 doi
Aprendizaje automático
Inteligencia artificial en medicina
Informática médica
Q325.5 / 2019 EB
Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings by Thuy T. Pham - 1 recurso en línea (XV, 107 páginas) 35 ilustraciones, 32 ilustraciones a color - Engineering (Springer-11647) Springer Theses Recognizing Outstanding Ph.D. Research 2190-5053 .
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.
9783319986753
10.1007/978-3-319-98675-3 doi
Aprendizaje automático
Inteligencia artificial en medicina
Informática médica
Q325.5 / 2019 EB