Multi-Objective Machine Learning
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly su...
Enregistré dans:
| Autres auteurs: | |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2006. |
| Collection: | Studies in Computational Intelligence
16 |
| 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: | • Multi-objective machine learning, Yaochu Jin (ed.), Berlin, Springer, 2006, 1 vol. (xiii, 660 p.), Studies in computational intelligence, 3-540-30676-5 • Multi-Objective Machine Learning, Texte imprimé, 9783642067969 • Multi-Objective Machine Learning, Texte imprimé, 9783540818359 • Multi-objective machine learning, Yaochu Jin (ed.), Berlin, Springer, 2006, 1 vol. (xiii, 660 p.), Studies in computational intelligence, 3-540-30676-5 |