### Abstract

We consider the problem of attribute-efficient learning in query and mistake-bound models. Attribute-efficient algorithms make a number of queries or mistakes that is polynomial in the number of relevant variables in the target function, but only sublinear in the number of irrelevant variables. We consider a variant of the membership query model in which the learning algorithm is given as input the number of relevant variables of the target function. Using a number-theoretic coloring technique, we show that in this model, any class of functions (including parity) that can be learned in polynomial time can be learned attribute-efficiently in polynomial time. We show that this does not hold in the randomized membership query model. In the mistake-bound model, we consider the problem of learning attribute-efficiently using hypotheses that are formulas of small depth. Our results extend the work of Blum et al. and Bshouty et al.

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

Editors | Anon |

Pages | 235-243 |

Number of pages | 9 |

State | Published - 1996 |

Event | Proceedings of the 1996 9th Annual Conference on Computational Learning Theory - Desenzano del Garda, Italy Duration: Jun 28 1996 → Jul 1 1996 |

### Other

Other | Proceedings of the 1996 9th Annual Conference on Computational Learning Theory |
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City | Desenzano del Garda, Italy |

Period | 6/28/96 → 7/1/96 |

### Fingerprint

### ASJC Scopus subject areas

- Computational Mathematics

### Cite this

*Proceedings of the Annual ACM Conference on Computational Learning Theory*(pp. 235-243)

**Attribute-efficient learning in query and mistake-bound models.** / Bshouty, Nader H.; Hellerstein, Lisa.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Proceedings of the Annual ACM Conference on Computational Learning Theory.*pp. 235-243, Proceedings of the 1996 9th Annual Conference on Computational Learning Theory, Desenzano del Garda, Italy, 6/28/96.

}

TY - CHAP

T1 - Attribute-efficient learning in query and mistake-bound models

AU - Bshouty, Nader H.

AU - Hellerstein, Lisa

PY - 1996

Y1 - 1996

N2 - We consider the problem of attribute-efficient learning in query and mistake-bound models. Attribute-efficient algorithms make a number of queries or mistakes that is polynomial in the number of relevant variables in the target function, but only sublinear in the number of irrelevant variables. We consider a variant of the membership query model in which the learning algorithm is given as input the number of relevant variables of the target function. Using a number-theoretic coloring technique, we show that in this model, any class of functions (including parity) that can be learned in polynomial time can be learned attribute-efficiently in polynomial time. We show that this does not hold in the randomized membership query model. In the mistake-bound model, we consider the problem of learning attribute-efficiently using hypotheses that are formulas of small depth. Our results extend the work of Blum et al. and Bshouty et al.

AB - We consider the problem of attribute-efficient learning in query and mistake-bound models. Attribute-efficient algorithms make a number of queries or mistakes that is polynomial in the number of relevant variables in the target function, but only sublinear in the number of irrelevant variables. We consider a variant of the membership query model in which the learning algorithm is given as input the number of relevant variables of the target function. Using a number-theoretic coloring technique, we show that in this model, any class of functions (including parity) that can be learned in polynomial time can be learned attribute-efficiently in polynomial time. We show that this does not hold in the randomized membership query model. In the mistake-bound model, we consider the problem of learning attribute-efficiently using hypotheses that are formulas of small depth. Our results extend the work of Blum et al. and Bshouty et al.

UR - http://www.scopus.com/inward/record.url?scp=0030389398&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030389398&partnerID=8YFLogxK

M3 - Chapter

SP - 235

EP - 243

BT - Proceedings of the Annual ACM Conference on Computational Learning Theory

A2 - Anon, null

ER -