Titlebook: Algorithmic Learning Theory; 26th International C Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles Conference proceedings 2015 Springer Int

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Der Ausschluss von Minderheitsaktionärenvestigated the learnability of linear sets and semilinear sets in three models – Valiant’s . model, Gold’s . model, and Angluin’s . model. This paper considers a . model of learning families of linear sets, whereby the learner is assumed to know all the smallest sets . of labelled examples that are

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https://doi.org/10.1007/978-3-8350-9248-8m of learning a DFA from given input data is a classic topic in computational learning theory. In this paper we study the learnability of a random DFA and propose a computationally efficient algorithm for learning and recovering a random DFA from uniform input strings and state information in the st

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,Thesenförmige Zusammenfassung,n a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution.

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Unterhaltungs- und Betriebskosten,the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered

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