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

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Algorithmic Probability: Theory and Applications,and subjectivity and show that its incomputability in no way inhibits its use for practical prediction. Applications to Bernoulli sequence prediction and grammar discovery are described. We conclude with a note on its employment in a very strong AI system for very general problem solving.

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978-1-4419-4650-8Springer-Verlag US 2009

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Information Approach to Blind Source Separation and Deconvolution, at time . et A is some transformation, which can be instantaneous, i.e. operating on each s(n) to produce x(n), or global (i.e. operating on the whole sequence s(·) of the source vectors. The transformation A is not completely arbitrary, one often assumes it belongs to a certain class ., the most p

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Information-Theoretic Causal Power,, we use a variation on the information-theoretic measure . to summarize the total causal influence of . on .. Our measure gives sensible results for a much wider variety of complex stochastic systems than previous attempts and promises to simplify the interpretation and application of Bayesian netw

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Information Divergence Geometry and the Application to Statistical Machine Learning,obustness. As applications to statistical learning we discuss the minimum divergence method for the principal component analysis, independent component analysis and for statistical pattern recognition.

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