### Abstract

In this paper, we introduce a new noise model on learning sets of strings in the framework of PAC learning and consider the effect of the noise on learning. The instance domain is the set Σ^{n} of strings over a finite alphabet Σ, and the examples are corrupted by purely random errors affecting only the instances (and not the labels). We consider three types of errors on instances, called EDIT operation errors. EDIT operations consist of `insertion', `deletion', and `change' of a symbol in a string. We call such a noise where the examples are corrupted by random errors of EDIT operations on instances the EDIT noise. First we show general upper bounds on the EDIT noise rate that a learning algorithm of taking the strategy of minimizing disagreements can tolerate and a learning algorithm can tolerate. Next we present an efficient algorithm that can learn a class of decision lists over the attributes `a string w contains a pattern p?' from noisy examples under some restriction on the EDIT noise rate.

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

Publisher | Publ by ACM |

Pages | 295-302 |

Number of pages | 8 |

ISBN (Print) | 089791497X |

Publication status | Published - 1992 |

Externally published | Yes |

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

### Other

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

Period | 92/7/27 → 92/7/29 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory*(pp. 295-302). Publ by ACM.

**Noise model on learning sets of strings.** / Sakakibara, Yasubumi; Siromoney, Rani.

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

*Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory.*Publ by ACM, pp. 295-302, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 92/7/27.

}

TY - GEN

T1 - Noise model on learning sets of strings

AU - Sakakibara, Yasubumi

AU - Siromoney, Rani

PY - 1992

Y1 - 1992

N2 - In this paper, we introduce a new noise model on learning sets of strings in the framework of PAC learning and consider the effect of the noise on learning. The instance domain is the set Σn of strings over a finite alphabet Σ, and the examples are corrupted by purely random errors affecting only the instances (and not the labels). We consider three types of errors on instances, called EDIT operation errors. EDIT operations consist of `insertion', `deletion', and `change' of a symbol in a string. We call such a noise where the examples are corrupted by random errors of EDIT operations on instances the EDIT noise. First we show general upper bounds on the EDIT noise rate that a learning algorithm of taking the strategy of minimizing disagreements can tolerate and a learning algorithm can tolerate. Next we present an efficient algorithm that can learn a class of decision lists over the attributes `a string w contains a pattern p?' from noisy examples under some restriction on the EDIT noise rate.

AB - In this paper, we introduce a new noise model on learning sets of strings in the framework of PAC learning and consider the effect of the noise on learning. The instance domain is the set Σn of strings over a finite alphabet Σ, and the examples are corrupted by purely random errors affecting only the instances (and not the labels). We consider three types of errors on instances, called EDIT operation errors. EDIT operations consist of `insertion', `deletion', and `change' of a symbol in a string. We call such a noise where the examples are corrupted by random errors of EDIT operations on instances the EDIT noise. First we show general upper bounds on the EDIT noise rate that a learning algorithm of taking the strategy of minimizing disagreements can tolerate and a learning algorithm can tolerate. Next we present an efficient algorithm that can learn a class of decision lists over the attributes `a string w contains a pattern p?' from noisy examples under some restriction on the EDIT noise rate.

UR - http://www.scopus.com/inward/record.url?scp=0026987081&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026987081&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0026987081

SN - 089791497X

SP - 295

EP - 302

BT - Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory

PB - Publ by ACM

ER -