Characterizations of Learnability for Classes of {0, ..., n)-Valued Functions

We investigate the PAC learnability of classes of {0, ..., n}-valued functions (n < ∞). For n = 1 it is known that the finiteness of the Vapnik-Chervonenkis dimension is necessary and sufficient for learning. For n > 1 several generalizations of the VC-dimension, each yielding a distinct chara...

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Veröffentlicht in:	Journal of computer and system sciences 1995-02, Vol.50 (1), p.74-86
Hauptverfasser:	Bendavid, S., Cesabianchi, N., Haussler, D., Long, P.M.
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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