Titlebook: Algorithmic Learning Theory; 24th International C Sanjay Jain,Rémi Munos,Thomas Zeugmann Conference proceedings 2013 Springer-Verlag Berlin

infringe

https://doi.org/10.1007/978-3-642-40935-6combinatorial optimization; gradient algorithms; online learning; reinforcement learning; universal arti

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性別

Algorithmic Learning Theory978-3-642-40935-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

格子架

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https://doi.org/10.1007/978-3-7091-2303-4in finite but exponentially many concepts and hence the complexity issue arises. In this paper, we survey some recent results on universal and efficient implementations of low-regret algorithmic frameworks such as Follow the Regularized Leader (FTRL) and Follow the Perturbed Leader (FPL).

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吸引力

https://doi.org/10.1007/978-3-7091-2303-4in finite but exponentially many concepts and hence the complexity issue arises. In this paper, we survey some recent results on universal and efficient implementations of low-regret algorithmic frameworks such as Follow the Regularized Leader (FTRL) and Follow the Perturbed Leader (FPL).

是比賽

https://doi.org/10.1007/978-3-7091-2303-4in time . returns the value .(.)?∈?.. Our goal is to exactly find (exactly learn) . with minimum number of queries and optimal time complexity. Or at least decide whether .?≡?. for some function .?∈?...This problem has different names in different areas: Interpolation, Exactly Learning, Inferring, I

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