Ensemble Machine Learning : Methods and Applications
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed ensemble learning by researchers in computational intelligence and machine learning, it is known to improve a decision system s robustness and...
Gardado en:
| Autor Principal: | |
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| Outros autores: | |
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer
[20..].
Cham : Springer Nature |
| Series: | Engineering Springer-11647
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| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Ensemble Machine Learning, Texte imprimé, 9781441993250 |
Table des matières:
- Introduction of Ensemble Learning Boosting Algorithms: Theory, Methods and Applications On Boosting Nonparametric Learners Super Learning Random Forest Ensemble Learning by Negative Correlation Learning Ensemble Nystrom Method Object Detection Ensemble Learning for Activity Recognition Ensemble Learning in Medical Applications Random Forest for Bioinformatics