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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Livro de bolso

2014, ISBN: 3642428754

[EAN: 9783642428753], Neubuch, [SC: 0.0], [PU: Springer Berlin Heidelberg], CLASSIFICATIONOFARCHAEOLOGICALCERAMICS; IMAGEPROCESSING; IMPACT-ECHOMEASUREMENTS; INDEPENDENTCOMPONENTANALYSIS(… mais…

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Addisson Salazar:

On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Livro de bolso

ISBN: 9783642428753

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 sp… mais…

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Addisson Salazar:
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Livro de bolso

2014

ISBN: 9783642428753

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 sp… mais…

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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Salazar, Addisson
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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - nuovo livro

2014, ISBN: 3642428754

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Addisson Salazar:
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Livro de bolso

ISBN: 9783642428753

Paperback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, s… mais…

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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
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9783642428753


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.

Dados detalhados do livro - On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling


EAN (ISBN-13): 9783642428753
ISBN (ISBN-10): 3642428754
Livro de capa dura
Livro de bolso
Ano de publicação: 2014
Editor/Editora: Springer Berlin

Livro na base de dados desde 2014-10-10T03:32:24-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2022-11-30T13:29:59-03:00 (Sao Paulo)
Número ISBN/EAN: 9783642428753

Número ISBN - Ortografia alternativa:
3-642-42875-4, 978-3-642-42875-3
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: addiss, salazar
Título do livro: statistica, com mix, statistical pattern recognition independent component analysis mixture modelling

Dados da editora

Autor: Addisson Salazar
Título: Springer Theses; On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
Editora: Springer; Springer Berlin
186 Páginas
Ano de publicação: 2014-08-09
Berlin; Heidelberg; DE
Impresso / Feito em
Peso: 3,226 kg
Língua: Inglês
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XXII, 186 p.

BC; Signal, Image and Speech Processing; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Elektronik; Verstehen; Classification of Archaeological Ceramics; Image Processing; Impact-echo Measurements; Independent Component Analysis (ICA); Independent Component Analysis Mixture; Machine Learning; Modelling (ICAMM); Non-parametric Density Estimation; PhD Thesis; Semi-supervised Learning; Statistical Pattern Recognition; complexity; Pattern Recognition; Complexity; Signal, Speech and Image Processing; Automated Pattern Recognition; Applied Dynamical Systems; Digitale Signalverarbeitung (DSP); Mustererkennung; Mathematik für Ingenieure; Kybernetik und Systemtheorie; BB

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.
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