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



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Éditeur :

Springer


Collection :

Springer Theses

Paru le : 2018-08-23



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Description


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.

Pages
107 pages
Collection
Springer Theses
Parution
2018-08-23
Marque
Springer
EAN papier
9783319986746
EAN PDF
9783319986753

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
4598 Ko
Prix
94,94 €
EAN EPUB
9783319986753

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
3441 Ko
Prix
94,94 €