Titlebook: Algorithmic Learning Theory; 23rd International C Nader H. Bshouty,Gilles Stoltz,Thomas Zeugmann Conference proceedings 2012 Springer-Verla

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Naturphilosophie und Naturwissenschaften,ivalent to uniform convergence of the empirical risk to the population risk, and that if a problem is learnable, it is learnable via empirical risk minimization. The equivalence of uniform convergence and learnability was formally established only in the supervised classification and regression sett

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978-3-642-34105-2Springer-Verlag Berlin Heidelberg 2012

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Algorithmic Learning Theory978-3-642-34106-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Editors’ Introductionhe development of programming languages for machine learning, and analyzing graph and network data. In his talk Declarative Modeling for Machine Learning and Data Mining he notes that despite the popularity of machine learning and data mining today, it remains challenging to develop applications and

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Declarative Modeling for Machine Learning and Data Miningdata mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algor

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