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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 2013 edition. 207 pages. 9.65x0.71x6.46 inches. In Stock.
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Taschenbuch. Zustand: Neu. On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling | Addisson Salazar | Taschenbuch | Springer Theses | xxii | Englisch | 2014 | Springer | EAN 9783642428753 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2014
ISBN 10: 3642428754 ISBN 13: 9783642428753
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2012
ISBN 10: 3642307515 ISBN 13: 9783642307515
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Sprache: Englisch
Verlag: NOVA SCIENCE PUBLISHERS INC (11/2018), 2018
ISBN 10: 1536139947 ISBN 13: 9781536139945
Anbieter: BOOKIT!, Genève, Schweiz
Zustand: Used: Like New. LIVRE A L?ETAT DE NEUF. EXPEDIE SOUS 3 JOURS OUVRES. NUMERO DE SUIVI COMMUNIQUE AVANT ENVOI, EMBALLAGE RENFORCE. EAN:9781536139945.