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Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces Read chapter Read first chapter

2010 | OriginalPaper | Chapter

5. Nonlinear Adaptive Filtering with MEE, MCC, and Applications

Authors : Deniz Erdogmus, Rodney Morejon, Weifeng Liu

Published in: Information Theoretic Learning

Publisher: Springer New York

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Abstract

Our emphasis on the linear model in Chapter 4 was only motivated by simplicity and pedagogy. As we have demonstrated in the simple case studies, under the linearity and Gaussianity conditions, the final solution of the MEE algorithms was basically equivalent to the solution obtained with the LMS. Because the LMS algorithm is computationally simpler and better understood, there is really no advantage to use MEE in such cases.

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previous chapter Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems
next chapter Classification with EEC, Divergence Measures, and Error Bounds
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Metadata
Title
Nonlinear Adaptive Filtering with MEE, MCC, and Applications
Authors
Deniz Erdogmus
Rodney Morejon
Weifeng Liu
Copyright Year
2010
Publisher
Springer New York
Book
Information Theoretic Learning

Print ISBN: 978-1-4419-1569-6

Electronic ISBN: 978-1-4419-1570-2

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-1-4419-1570-2_5

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