Titlebook: Computational Learning Theory; 4th European Confere Paul Fischer,Hans Ulrich Simon Conference proceedings 1999 Springer-Verlag Berlin Heide

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书目名称Computational Learning Theory影响因子(影响力)




书目名称Computational Learning Theory影响因子(影响力)学科排名




书目名称Computational Learning Theory网络公开度




书目名称Computational Learning Theory网络公开度学科排名




书目名称Computational Learning Theory被引频次




书目名称Computational Learning Theory被引频次学科排名




书目名称Computational Learning Theory年度引用




书目名称Computational Learning Theory年度引用学科排名




书目名称Computational Learning Theory读者反馈




书目名称Computational Learning Theory读者反馈学科排名

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https://doi.org/10.1007/978-3-322-87794-9hted average of the experts’ predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most . ln ., where . is the number of experts and . a constant that depends on the loss fun

cleaver

https://doi.org/10.1007/978-3-658-00686-0operly between hyperrobust Ex-learning and hyperrobust BC-learning. Furthermore, the bounded totally reliably BC-learnable classes are characterized in terms of infinite branches of certain enumerable families of bounded recursive trees. A class of infinite branches of a further family of trees sepa

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https://doi.org/10.1007/978-3-658-00686-0lds a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number . > 0, the class of languages defined by formal systems of length ≤ .:.The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal

giggle

Open Theoretical Questions in Reinforcement Learning in a given state and ending upon arrival in a terminal state, terminating the series above. In other cases the interaction is continual, without interruption, and the sum may have an infinite number of terms (in which case we usually assume γ < 1). Infinite horizon cases with γ = 1 are also possibl

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Averaging Expert Predictionshted average of the experts’ predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most . ln ., where . is the number of experts and . a constant that depends on the loss fun