### Abstract

Original language | English |
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Title of host publication | Weightless Neural Network Workshop'95, Computing with Logical Neurons |

Editors | David Bisset |

Place of Publication | Canterbury |

Publisher | University of Kent |

Pages | 93-102 |

Number of pages | 10 |

Publication status | Published - Sep 1995 |

Event | Proceedings of the Weightless Neural Network Workshop 1995, Computing with Logical Neurons - Duration: 1 Sep 1995 → 1 Sep 1995 |

### Workshop

Workshop | Proceedings of the Weightless Neural Network Workshop 1995, Computing with Logical Neurons |
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Period | 1/09/95 → 1/09/95 |

### Fingerprint

### Keywords

- good-turing
- zero frequency
- estimates
- maximum likelihood estimation

### Cite this

*Weightless Neural Network Workshop'95, Computing with Logical Neurons*(pp. 93-102). Canterbury: University of Kent.

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*Weightless Neural Network Workshop'95, Computing with Logical Neurons.*University of Kent, Canterbury, pp. 93-102, Proceedings of the Weightless Neural Network Workshop 1995, Computing with Logical Neurons, 1/09/95.

**Good-turing estimation for the frequentist n-tuple classifier.** / Morciniec, Michal; Rohwer, Richard.

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

TY - CHAP

T1 - Good-turing estimation for the frequentist n-tuple classifier

AU - Morciniec, Michal

AU - Rohwer, Richard

PY - 1995/9

Y1 - 1995/9

N2 - We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.

AB - We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.

KW - good-turing

KW - zero frequency

KW - estimates

KW - maximum likelihood estimation

M3 - Chapter

SP - 93

EP - 102

BT - Weightless Neural Network Workshop'95, Computing with Logical Neurons

A2 - Bisset, David

PB - University of Kent

CY - Canterbury

ER -