On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling: 4 - Rilegato

Salazar, Addisson

 
9783642307515: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling: 4
Rilegato
ISBN 10: 3642307515 ISBN 13: 9783642307515
Casa editrice: Springer Nature, 2012

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2012
Lingua
Inglese
ISBN 10 
3642307515
ISBN 13 
9783642307515
Rilegatura
Copertina rigida
Numero di pagine
185
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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Altre edizioni note dello stesso titolo

9783642428753: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling: 4

Edizione in evidenza

ISBN 10:  3642428754 ISBN 13:  9783642428753
Casa editrice: Springer, 2014
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