TR#: | CIS9110 |

Class: | CIS |

Title: | DOMINATING DISTRIBUTIONS AND
LEARNABILITY |

Authors: | G.Y. Benedek and A. Itai |

Not Available | |

Abstract: | We consider the PAC-learning model first introduced by Valiant, but assume that the distribution is known to the student. The problem addressed here is characterizing when learnability with respect to distribution $D_1$ implies learnability with respect to distribution $D_2$. The answer to the above question depends on the learnability model. If the number of examples need not be bounded by a polynomial, it is sufficient to require that all sets which have zero probability with respect to $D_2$ have zero probability with respect to $D_1$. If the number of examples is required to be polynomial, then the probability with respect to $D_2$ must be bounded by a multiplicative constant from that of $D_1$. More stringent conditions must hold if we insist that every hypothesis consistent with the examples be close to the target. Finally, we address the learnability properties of classes of distributions. |

Copyright | The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information |

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