000 04509nam a22005175i 4500
001 978-1-4419-1570-2
003 DE-He213
005 20140220084506.0
007 cr nn 008mamaa
008 100427s2010 xxu| s |||| 0|eng d
020 _a9781441915702
_9978-1-4419-1570-2
024 7 _a10.1007/978-1-4419-1570-2
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aPrincipe, Jose C.
_eauthor.
245 1 0 _aInformation Theoretic Learning
_h[electronic resource] :
_bRenyi's Entropy and Kernel Perspectives /
_cby Jose C. Principe.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2010.
300 _aXIV, 448p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Science and Statistics,
_x1613-9011
505 0 _aInformation Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces -- Renyi’s Entropy, Divergence and Their Nonparametric Estimators -- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria -- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems -- Nonlinear Adaptive Filtering with MEE, MCC, and Applications -- Classification with EEC, Divergence Measures, and Error Bounds -- Clustering with ITL Principles -- Self-Organizing ITL Principles for Unsupervised Learning -- A Reproducing Kernel Hilbert Space Framework for ITL -- Correntropy for Random Variables: Properties and Applications in Statistical Inference -- Correntropy for Random Processes: Properties and Applications in Signal Processing.
520 _aThis book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings, respectively entropy, mutual information and correntropy. ITL quantifies the stochastic structure of the data beyond second order statistics for improved performance without using full-blown Bayesian approaches that require a much larger computational cost. This is possible because of a non-parametric estimator of Renyi’s quadratic entropy that is only a function of pairwise differences between samples. The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications. Students, practitioners and researchers interested in statistical signal processing, computational intelligence, and machine learning will find in this book the theory to understand the basics, the algorithms to implement applications, and exciting but still unexplored leads that will provide fertile ground for future research. José C. Principe is Distinguished Professor of Electrical and Biomedical Engineering, and BellSouth Professor at the University of Florida, and the Founder and Director of the Computational NeuroEngineering Laboratory. He is an IEEE and AIMBE Fellow, Past President of the International Neural Network Society, Past Editor-in-Chief of the IEEE Trans. on Biomedical Engineering and the Founder Editor-in-Chief of the IEEE Reviews on Biomedical Engineering. He has written an interactive electronic book on Neural Networks, a book on Brain Machine Interface Engineering and more recently a book on Kernel Adaptive Filtering, and was awarded the 2011 IEEE Neural Network Pioneer Award.
650 0 _aEngineering.
650 0 _aRemote sensing.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aComputational Intelligence.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aRemote Sensing/Photogrammetry.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441915696
830 0 _aInformation Science and Statistics,
_x1613-9011
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-1570-2
912 _aZDB-2-SCS
999 _c110427
_d110427