Supervised and Unsupervised Ensemble Methods and their Applications
This book was inspired by the last argument and resulted from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) organized on June 4, 2007 in Girona, Spain. This workshop was held in conjunction with the 3rd Iberian Conference on Pattern Recognition...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Andre forfattere: | |
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Udgivelse: | 1st ed. 2008. |
| Serier: | Studies in Computational Intelligence
126 |
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Supervised and unsupervised ensemble methods and their applications, Oleg Okun, Giorgio Valentini (eds.), 2008, Berlin, Springer, 1 vol. (180 pages), Studies in computational intelligence, 978-3-540-78980-2 • Supervised and Unsupervised Ensemble Methods and their Applications, Texte imprimé, 9783642097768 • Supervised and Unsupervised Ensemble Methods and their Applications, Texte imprimé, 9783540849650 • Supervised and unsupervised ensemble methods and their applications, Oleg Okun, Giorgio Valentini (eds.), 2008, Berlin, Springer, 1 vol. (180 pages), Studies in computational intelligence, 978-3-540-78980-2 |
Indholdsfortegnelse:
- Ensembles of Clustering Methods and Their Applications Cluster Ensemble Methods: from Single Clusterings to Combined Solutions Random Subspace Ensembles for Clustering Categorical Data Ensemble Clustering with a Fuzzy Approach Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis Ensembles of Classification Methods and Their Applications Intrusion Detection in Computer Systems Using Multiple Classifier Systems Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification Multivariate Time Series Classification via Stacking of Univariate Classifiers Gradient Boosting GARCH and Neural Networks for Time Series Prediction Cascading with VDM and Binary Decision Trees for Nominal Data Erratum