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Titlebook: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling; Addisson Salazar Book 2013 Springer-Verlag Berlin

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书目名称On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
编辑Addisson Salazar
视频video
概述Nominated as an outstanding PhD theses by the Polytechnic University of Valencia.Present an excellent state-of-the-art literature review of the main applied theoretical foundations of statistical patt
丛书名称Springer Theses
图书封面Titlebook: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling;  Addisson Salazar Book 2013 Springer-Verlag Berlin
描述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.
出版日期Book 2013
关键词Classification of Archaeological Ceramics; Image Processing; Impact-echo Measurements; Independent Comp
版次1
doihttps://doi.org/10.1007/978-3-642-30752-2
isbn_softcover978-3-642-42875-3
isbn_ebook978-3-642-30752-2Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightSpringer-Verlag Berlin Heidelberg 2013
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Hierarchical Clustering from ICA Mixtures, nested clusters of various sizes. The whole node of the dendrogram represents the whole data set. The internal nodes describe the extent that the objects are proximal to each other; and the height of the dendrogram usually represents the distance between each pair of objects or clusters, or an obje
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