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Addisson Salazar
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
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2635994On Statistical Pattern Recognition in Independent Component Analysis Mixture Modellinghttps://www.gandhi.com.mx/on-statistical-pattern-recognition-in-independent-component-analysis-mixture-modelling-9783642307522/phttps://gandhi.vtexassets.com/arquivos/ids/3396943/ba048564-b5ae-46b2-b5cd-327c3b9419a2.jpg?v=63838531557777000017321925MXNSpringer Berlin HeidelbergInStock/Ebooks/<p>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.</p>...2572020On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling17321925https://www.gandhi.com.mx/on-statistical-pattern-recognition-in-independent-component-analysis-mixture-modelling-9783642307522/phttps://gandhi.vtexassets.com/arquivos/ids/3396943/ba048564-b5ae-46b2-b5cd-327c3b9419a2.jpg?v=638385315577770000InStockMXN99999DIEbook20129783642307522_W3siaWQiOiJkODBhNmNlNC1kZDFkLTQ1ZWMtODI5YS0wMWY4ZjRlMmYxY2IiLCJsaXN0UHJpY2UiOjE5MjUsImRpc2NvdW50IjoxOTMsInNlbGxpbmdQcmljZSI6MTczMiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDUtMjhUMTk6MDA6MDBaIiwidG8iOiIyMDI1LTA2LTMwVDIzOjU5OjU5WiIsInJlZ2lvbiI6Ik1YIiwiaXNQcmVvcmRlciI6ZmFsc2V9LHsiaWQiOiIyNmQwNGY1My00ZTRhLTQ1ZTAtODE4OC00ZDNmNTlhNWExN2MiLCJsaXN0UHJpY2UiOjE5NTEsImRpc2NvdW50IjoxOTUsInNlbGxpbmdQcmljZSI6MTc1NiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDctMDFUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9783642307522_<p>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.</p>(*_*)9783642307522_<p>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.</p>...9783642307522_Springer Berlin Heidelberglibro_electonico_f6985f74-8a01-3297-8eae-a4f6497114e9_9783642307522;9783642307522_9783642307522Addisson SalazarInglésMéxico2012-07-20T00:00:00+00:00Springer Berlin Heidelberg