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...
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| Autres auteurs: | |
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
New York, NY :
Springer New York : Imprint: Springer
[20..].
Cham : Springer Nature |
| Collection: | Engineering Springer-11647
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| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
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 |
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| 100 | 1 | |a Zhang, Cha. | |
| 245 | 1 | 0 | |a Ensemble Machine Learning : |b Methods and Applications |c edited by Cha Zhang, Yunqian Ma. |
| 260 | |a New York, NY : |b Springer New York : |b Imprint: Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 300 | |a 1 vol. (331 p.). | ||
| 490 | 0 | |a Engineering Springer-11647 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Index | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as boosting and random forest facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike. | ||
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| 776 | 0 | |t Ensemble Machine Learning |b Texte imprimé |z 9781441993250 | |
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