Titlebook: Algorithmic Learning Theory; 14th International C Ricard Gavaldá,Klaus P. Jantke,Eiji Takimoto Conference proceedings 2003 Springer-Verlag

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書(shū)目名稱Algorithmic Learning Theory影響因子(影響力)




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書(shū)目名稱Algorithmic Learning Theory被引頻次




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Can Learning in the Limit Be Done Efficiently?the learner did already succeed. The resulting uncertainty may be prohibitive in many applications. We survey results to resolve this problem by outlining a new learning model, called .. Though pattern languages can neither be finitely inferred from positive data nor PAC-learned, our approach can be

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自愛(ài)

On Ordinal VC-Dimension and Some Notions of Complexityomplexity—a variation on predictive complexity—and mind change complexity. The assumptions that . is closed under boolean operators and that . is compact often play a crucial role to establish connections between these concepts.

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On the Learnability of Erasing Pattern Languages in the Query Model efficient than standard query learners. Moreover, when studying this new model in a more general context, interesting relations to Gold’s model of language learning from only positive data have been elaborated.

Terminal

Efficiently Learning the Metric with Side-Informationmputational complexity of the method severely limits its applicability to real machine learning tasks. In this paper we present an alternative solution for dealing with the problem of incorporating side-information. This side-information specifies pairs of examples belonging to the same class. The a

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