发帖
雷达卡
English | 2018 | ISBN: 3319986746 | 107 Pages | True PDF | 4.44 MB
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.
applying-ml-automated-classification-biomedical.pdf
(4.45 MB, 需要: 10 个论坛币)
京公网安备 11010802022788号
