### Abstract

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of both positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for learning in a model for problems involving hidden variables.

Original language | English (US) |
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Title of host publication | Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory |

Publisher | Publ by ACM |

Pages | 341-352 |

Number of pages | 12 |

ISBN (Print) | 089791497X, 9780897914970 |

DOIs | |

State | Published - Jan 1 1992 |

Externally published | Yes |

Event | Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory - Pittsburgh, PA, USA Duration: Jul 27 1992 → Jul 29 1992 |

### Publication series

Name | Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory |
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### Other

Other | Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory |
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City | Pittsburgh, PA, USA |

Period | 7/27/92 → 7/29/92 |

### All Science Journal Classification (ASJC) codes

- Engineering(all)

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## Cite this

*Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory*(pp. 341-352). (Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory). Publ by ACM. https://doi.org/10.1145/130385.130424