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Hardcover - 9783319986746
106,99 €
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Beschreibung
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
| Verlag | Springer International Publishing |
| Ersterscheinung | 25. Januar 2019 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 201 Gramm |
| Format | Softcover |
| ISBN-13 | 9783030075187 |
| Auflage | Softcover reprint of the original 1st ed. 2019 |
| Seiten | 107 |
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