Applying Machine Learning For Automated Classification Of Biomedical Data In Subject Independent Settings

Catergory: Computer Technology, Medical, Nonfiction

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.

🌞 Contents of Download:
📌 3319986740.epub (Thuy T. Pham) (2019) (3.36 MB)
📌 3319986740.pdf (Thuy T. Pham) (2018) (4.45 MB)

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⭐️ Applying Machine Learning For Automated Classification Of Biomedical Data In Subject Independent Settings ✅ (7.81 MB)
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