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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings - Springer Theses (Paperback)
  • Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings - Springer Theses (Paperback)
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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings - Springer Theses (Paperback)

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£109.99
Paperback 107 Pages / Published: 25/01/2019
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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.


Publisher: Springer Nature Switzerland AG
ISBN: 9783030075187
Number of pages: 107
Weight: 203 g
Dimensions: 235 x 155 mm
Edition: Softcover reprint of the original 1st ed. 201

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