Springer Theses

Pham, Thuy T.

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.

Details

Published by: Springer

Publication Date: 2018-08-31

Format: Hardcover

ISBN-13: 9783319986746

DOI: 10.1007/978-3-319-98675-3

Dimensions: 235cm x155cm

Pages: 107