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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | Springer Theses, Recognizing Outstanding Ph.D. Research,
4 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-642-30752-2 | ||
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| 005 | 20220114095214.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 120720s2013 gw | s |||| 0|eng d | ||
| 020 | |a 9783642307522 |9 978-3-642-30752-2 | ||
| 024 | 7 | |a 10.1007/978-3-642-30752-2 |2 doi | |
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| 100 | 1 | |a Salazar, Addisson. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling |h [electronic resource] / |c by Addisson Salazar. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2013. | |
| 300 | |a XXII, 186 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 ; |v 4 | |
| 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. | |
| 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. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Dynamics. | |
| 650 | 0 | |a Nonlinear theories. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Applied Dynamical Systems. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642307539 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642428753 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642307515 |
| 830 | 0 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 ; |v 4 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-30752-2 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||