### Abstract

In the framework of PAC-learning model, relationships between learning processes and information compressing processes are investigated. Information compressing processes are formulated as weak Occam algorithms. A weak Occam algorithm is a deterministic polynomial time algorithm that, when given m examples of unknown function, outputs, with high probability, a representation of a function that is consistent with the examples and belongs to a function class with complexity o(m). It has been shown that a weak Occam algorithm is also a consistent PAC-learning algorithm. In this extended abstract, it is shown that the converse does not hold by giving a PAC-learning algorithm that is not a weak Occam algorithm, and also some natural properties, called conservativeness and monotonicity, for learning algorithms that might help the converse hold are given. In particular, the conditions that make a conservative PAC-learning algorithm a weak Occam algorithm are given, and it is shown that, under some natural conditions, a monotone PAC-learning algorithm for a hypothesis class can be transformed to a weak Occam algorithm without changing the hypothesis class.

Original language | English |
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Title of host publication | Proc 6 Annu ACM Conf Comput Learn Theory |

Publisher | Publ by ACM |

Pages | 377-383 |

Number of pages | 7 |

ISBN (Print) | 0897916115, 9780897916110 |

DOIs | |

Publication status | Published - 1993 |

Externally published | Yes |

Event | Proceedings of the 6th Annual ACM Conference on Computational Learning Theory - Santa Cruz, CA, USA Duration: Jul 26 1993 → Jul 28 1993 |

### Publication series

Name | Proc 6 Annu ACM Conf Comput Learn Theory |
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### Other

Other | Proceedings of the 6th Annual ACM Conference on Computational Learning Theory |
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City | Santa Cruz, CA, USA |

Period | 7/26/93 → 7/28/93 |

### All Science Journal Classification (ASJC) codes

- Engineering(all)

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## Cite this

*Proc 6 Annu ACM Conf Comput Learn Theory*(pp. 377-383). (Proc 6 Annu ACM Conf Comput Learn Theory). Publ by ACM. https://doi.org/10.1145/168304.168381