On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Science & Nature, Technology, Electronics
Cover of the book On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling by Addisson Salazar, Springer Berlin Heidelberg
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Addisson Salazar ISBN: 9783642307522
Publisher: Springer Berlin Heidelberg Publication: July 20, 2012
Imprint: Springer Language: English
Author: Addisson Salazar
ISBN: 9783642307522
Publisher: Springer Berlin Heidelberg
Publication: July 20, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer Berlin Heidelberg

Cover of the book Interaktive Großgruppen by Addisson Salazar
Cover of the book Anwendungen und Technik von Near Field Communication (NFC) by Addisson Salazar
Cover of the book The Dawn Angiosperms by Addisson Salazar
Cover of the book The Tropospheric Chemistry of Ozone in the Polar Regions by Addisson Salazar
Cover of the book Erfolgsfaktoren für eine digitale Zukunft by Addisson Salazar
Cover of the book QCM-D Studies on Polymer Behavior at Interfaces by Addisson Salazar
Cover of the book Anaphora Resolution by Addisson Salazar
Cover of the book Robust Output Feedback H-infinity Control and Filtering for Uncertain Linear Systems by Addisson Salazar
Cover of the book Industrielle Mikrobiologie by Addisson Salazar
Cover of the book Baltic Coastal Ecosystems by Addisson Salazar
Cover of the book Shape Reconstruction from Apparent Contours by Addisson Salazar
Cover of the book The Origin of the Galaxy and Local Group by Addisson Salazar
Cover of the book Diskrete Mathematik: Geordnete Mengen by Addisson Salazar
Cover of the book Verkehrssicherheit by Addisson Salazar
Cover of the book Praxis der Manuellen Medizin bei Säuglingen und Kindern by Addisson Salazar
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy