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Books > Science & Mathematics > Biology, life sciences > Molecular biology
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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings (Paperback, Softcover reprint of the original 1st ed. 2019)
Loot Price: R2,860
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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings (Paperback, Softcover reprint of the original 1st ed. 2019)
Series: Springer Theses
Expected to ship within 10 - 15 working days
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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.
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