On statistical pattern recognition in independent component analysis mixture modelling
A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2013.
Cham : Springer Nature |
| Serier: | Springer Theses, Recognizing Outstanding Ph.D. Research
4 |
| 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: | • On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling, Texte imprimé, 9783642307515 • On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling, Texte imprimé, 9783642307539 • On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling, Texte imprimé, 9783642428753 |
Indholdsfortegnelse:
- Introduction
- ICA and ICAMM Methods
- Learning Mixtures of Independent Component Analysers
- Hierarchical Clustering from ICA Mixtures
- Application of ICAMM to Impact-Echo Testing
- Cultural Heritage Applications: Archaeological Ceramics and Building Restoration
- Other Applications: Sequential Dependence Modelling and Data Mining
- Conclusions.