Titlebook: Information Theoretic Learning; Renyi‘s Entropy and Jose C. Principe Book 2010 Springer-Verlag New York 2010 Correntropy.Information theor

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Puskal Pokharel,Ignacio Santamaria,Jianwu Xu,Kyu-hwa Jeong,Weifeng Liu

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1613-9011 t 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, res

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Clustering with ITL Principles,uishable macroscopically by the shape of the probability density function. Therefore, entropy and the concept of dissimilarity naturally form the foundations for unsupervised learning because they are descriptors of PDFs.

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Correntropy for Random Variables: Properties and Applications in Statistical Inference,r moment descriptors of the probability density function really are in scientific thinking. Successful engineering or pattern recognition solutions from these methodologies rely heavily on the Gaussianity and linearity assumptions, exactly for the same reasons discussed in Chapter 3.

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Book 2010h 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 c

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