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A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higherorder information. Thus, highorder spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for nonlinear processing of data with complex nonGaussian 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, impactecho testing, cultural heritage, hypnograms analysis, webmining and might therefore be employed to solve many different realworld problems.
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birth defects, or other reproductive harm.
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