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

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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

By: Addisson Salazar

eText | 20 July 2012

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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.

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You Can Find This eBook In

Non-FictionEngineering & TechnologyElectronics & Communications EngineeringElectronics EngineeringOther Technologies & Applied SciencesApplied OpticsImaging Systems & TechnologyComputing & I.T.Computer ScienceArtificial IntelligenceComputer VisionTechnology in General
Engineering in GeneralPattern RecognitionReference, Information & Interdisciplinary SubjectsResearch & InformationInformation theoryCybernetics & Systems TheoryImage ProcessingLibrary & Info SciencesLibrary & Information ServicesDigital Signal Processing (DSP)Maths for Engineers

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