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## Abstract

We introduce a new notion called Fourier-accessibility that allows us to precisely characterize the class of Boolean functions for which a standard greedy learning algorithm successfully learns all relevant attributes. If the target function is Fourier-accessible, then the success probability of the greedy algorithm can be made arbitrarily close to one. On the other hand, if the target function is not Fourier-accessible, then the error probability tends to one. Finally, we extend these results to the situation where the input data are corrupted by random attribute and classification noise and prove that greedy learning is quite robust against such errors.

Original language | English |
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Title of host publication | COCOON 2007: Computing and Combinatorics |

Number of pages | 11 |

Volume | 4598 LNCS |

Publisher | Springer Berlin Heidelberg |

Publication date | 01.12.2007 |

Pages | 296-306 |

ISBN (Print) | 978-3-540-73544-1 |

ISBN (Electronic) | 978-3-540-73545-8 |

DOIs | |

Publication status | Published - 01.12.2007 |

Event | 13th Annual International Computing and Combinatorics Conference - Banff, Canada Duration: 16.07.2007 → 19.07.2007 Conference number: 70851 |

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Dive into the research topics of 'When Does Greedy Learning of Relevant Attributes Succeed? A Fourier-Based Characterization'. Together they form a unique fingerprint.## Projects

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