Titlebook: Empirical Inference; Festschrift in Honor Bernhard Schölkopf,Zhiyuan Luo,Vladimir Vovk Book 2013 Springer-Verlag Berlin Heidelberg 2013 Bay

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PAC-Bayesian TheoryThe PAC-Bayesian framework is a frequentist approach to machine learning which encodes learner bias as a “prior probability” over hypotheses. This chapter reviews basic PAC-Bayesian theory, including Catoni’s basic inequality and Catoni’s localization theorem.

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978-3-662-52511-1Springer-Verlag Berlin Heidelberg 2013

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Bernhard Schölkopf,Zhiyuan Luo,Vladimir VovkHonours one of the pioneers of machine learning.Contributing authors are among the leading authorities in these domains.Of interest to researchers and engineers in the fields of machine learning, stat

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Sonnenbad, Schlaf und Rhythmus,te of Control Sciences of the Russian Academy of Sciences, Moscow, Russia) in the framework of the “Generalised Portrait Method” for computer learning and pattern recognition. The development of these ideas started in 1962 and they were first published in 1964.

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Die Zelle als metabolisches System,d simple estimators of the minimum mean squared error Mean squared error—(Minimum mean squared error—(., and prove their strong consistenciesConsistency—(. We bound the rate of convergenceRate of convergence, too.

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Early History of Support Vector Machineste of Control Sciences of the Russian Academy of Sciences, Moscow, Russia) in the framework of the “Generalised Portrait Method” for computer learning and pattern recognition. The development of these ideas started in 1962 and they were first published in 1964.