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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
Salazar, Addisson; Vergara, Luis; Igual, Jorge
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Description for On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
Hardback. 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). Series: Springer Theses. Num Pages: 208 pages, biography. BIC Classification: UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 12. Weight in Grams: 479.
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 ... Read more
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 ... Read more
Product Details
Format
Hardback
Publication date
2012
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
208
Condition
New
Series
Springer Theses
Number of Pages
186
Place of Publication
Berlin, Germany
ISBN
9783642307515
SKU
V9783642307515
Shipping Time
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Ref
99-15
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