On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling (Hardcover, 2013 ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Springer Theses, 4
Release date:	July 2012
First published:	2013
Authors:	Addisson Salazar
Dimensions:	235 x 155 x 12mm (L x W x T)
Format:	Hardcover
Pages:	186
Edition:	2013 ed.
ISBN-13:	978-3-642-30751-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Promotions
LSN:	3-642-30751-5
Barcode:	9783642307515

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