On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling - Couverture souple

Salazar, Addisson

Couverture souple

ISBN 10 : 3642428754 ISBN 13 : 9783642428753

Editeur : Springer, 2014

Synopsis

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.

A propos du livre

Présentation de l'éditeur

This outstanding review of the literature on the core theoretical foundations of applied statistical pattern recognition defines a novel mode of pattern recognition and classification, based on independent component analysis mixture modeling (ICAMM).

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 3642428754
• ISBN 13: 9783642428753
• Reliure: Broché
• Nombre de pages: 208
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne