Data mining : foundations and intelligent paradigms Volume 2, Statistical, bayesian, time series and other theoretical aspects
Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisti...
Enregistré dans:
| Autres auteurs: | , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2012. |
| Collection: | Intelligent Systems Reference Library
24 |
| 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: | • Data mining, foundations and intelligent paradigms, Vol. 2, Statistical, Bayesian, time series and other theoretical aspects, Dawn E. Holmes et Lakhmi C. Jain, Berlin, Springer, 2012, 1 vol. (XII, 246 p.), Intelligent systems reference library, 978-3-642-23240-4 • Data Mining: Foundations and Intelligent Paradigms, Texte imprimé, 9783642434297 • Data mining, foundations and intelligent paradigms, Vol. 2, Statistical, Bayesian, time series and other theoretical aspects, Dawn E. Holmes et Lakhmi C. Jain, Berlin, Springer, 2012, 1 vol. (XII, 246 p.), Intelligent systems reference library, 978-3-642-23240-4 • Data Mining: Foundations and Intelligent Paradigms, Texte imprimé, 9783642232428 |
Table des matières:
- From the content: Data Mining with Multilayer Perceptrons and Support Vector Machines Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid Formal framework for the Study of Algorithmic Properties of Objective Interestingness Measures Nonnegative Matrix Factorization: Models, Algorithms and Applications Visual Data Mining and Discovery with Binarized Vectors