Titlebook: Information Theory and Statistical Learning; Frank Emmert-Streib,Matthias Dehmer Book 2009 Springer-Verlag US 2009 algorithms.combinatoria

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Information Theoretic Learning and Kernel Methods,ic learning and the Mercer kernel methods. We show that Parzen windowing for estimation of probability density functions reveals the connections, enabling the information theoretic criteria to be expressed in terms of mean vectors in a Mercer kernel feature space, or equivalently, in terms of kernel

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Information Divergence Geometry and the Application to Statistical Machine Learning,ation divergence indices that express quantitatively a departure between any two probability density functions. In general, the information divergence leads to a statistical method by minimization which is based on the empirical data available. We discuss the association between the information dive

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Extreme Physical Information as a Principle of Universal Stability,. consisting of a theoretical system A, in a state ., that interacts with a system . that may be an observer. Both . and . are assumed to be real systems. The interaction is via a probe particle, which carries information about . to ., and in doing so perturbs the total system . in two ways - (1) It

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https://doi.org/10.1007/978-0-387-84816-7algorithms; combinatorial optimization; data compression; information; information theory; kernel method;

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