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:
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|---|---|
| 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 |
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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 |
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| 009 | PPN168317591 | ||
| 020 | |a 9783642307522 | ||
| 041 | 0 | |a eng | |
| 082 | |a 621.382 | ||
| 100 | 1 | |a Salazar, Addisson, |d 19..- | |
| 245 | 1 | 0 | |a On statistical pattern recognition in independent component analysis mixture modelling |c by Addisson Salazar. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2013. | ||
| 490 | 0 | |a Springer Theses, Recognizing Outstanding Ph.D. Research |v 4 |x 2190-5061 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a 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. | |
| 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. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems. | ||
| 776 | 0 | |t On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling |b Texte imprimé |z 9783642307515 | |
| 776 | 0 | |t On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling |b Texte imprimé |z 9783642307539 | |
| 776 | 0 | |t On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling |b Texte imprimé |z 9783642428753 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-30752-2 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-3C7Q5XMQ-4 |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:748071512 |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.1007/978-3-642-30752-2 |z Accès Université d'Orléans | |
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