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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling by Addisson Salazar
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling by Addisson Salazar
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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

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Description

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.
Release date Australia
August 9th, 2014
Audience
  • Professional & Vocational
Illustrations
XXII, 186 p.
Pages
186
Dimensions
155x235x11
ISBN-13
9783642428753
Product ID
22697681

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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling on Hardback by Addisson Salazar Save $5
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