Titlebook: Computational Learning Theory; Third European Confe Shai Ben-David Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1997 Algor

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Zielsetzung und Problemstellung,et al. [7], it is assumed that membership queries on instances near the boundary of the target concept may receive a “don‘t know” answer..We show that zero-one threshold functions are efficiently learnable in this model. The learning algorithm uses split graphs when the boundary region has radius 1,

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Zusammenfassung und Diskussion,m of machine learning using randomly drawn examples. Quite often in practice some form of . partial information about the target is available in addition to randomly drawn examples. In this paper we extend the PAC model to a scenario of learning with partial information in addition to randomly drawn

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Diskussion ausgew?hlter Beispieleandomized hypotheses for learning with small sample sizes and high malicious noise rates. We show an algorithm that PAC learns any target class of VC-dimension . using randomized hypotheses and order of . training examples (up to logarithmic factors) while tolerating malicious noise rates even sligh

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