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.
| Autorius: | Thuy T. Pham |
| Serija: | Springer Theses |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2019 |
| Knygos puslapių skaičius: | 124 |
| ISBN-10: | 3030075184 |
| ISBN-13: | 9783030075187 |
| Formatas: | 235 x 155 x 8 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings“
