### Abstract

We present a general method for selecting a small set of training inputs, the observations of which will suffice to estimate the parameters of a given linear model. We exemplify the algorithm in terms of predicting segmental duration of phonetic-segment feature vectors in a text-to-speech synthesizer, but the algorithm will work for any linear model and its associated domain.

Original language | English (US) |
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Title of host publication | Proceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996 |

Publisher | Association for Computing Machinery |

Pages | 288-295 |

Number of pages | 8 |

Volume | Part F129447 |

ISBN (Electronic) | 0898713668 |

State | Published - Jan 28 1996 |

Externally published | Yes |

Event | 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996 - Atlanta, United States Duration: Jan 28 1996 → Jan 30 1996 |

### Other

Other | 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996 |
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Country | United States |

City | Atlanta |

Period | 1/28/96 → 1/30/96 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Mathematics(all)

### Cite this

*Proceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996*(Vol. Part F129447, pp. 288-295). Association for Computing Machinery.

**Selecting training inputs via greedy rank covering.** / Buchsbaum, Adam L.; Van Santen, Jan.

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

*Proceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996.*vol. Part F129447, Association for Computing Machinery, pp. 288-295, 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996, Atlanta, United States, 1/28/96.

}

TY - GEN

T1 - Selecting training inputs via greedy rank covering

AU - Buchsbaum, Adam L.

AU - Van Santen, Jan

PY - 1996/1/28

Y1 - 1996/1/28

N2 - We present a general method for selecting a small set of training inputs, the observations of which will suffice to estimate the parameters of a given linear model. We exemplify the algorithm in terms of predicting segmental duration of phonetic-segment feature vectors in a text-to-speech synthesizer, but the algorithm will work for any linear model and its associated domain.

AB - We present a general method for selecting a small set of training inputs, the observations of which will suffice to estimate the parameters of a given linear model. We exemplify the algorithm in terms of predicting segmental duration of phonetic-segment feature vectors in a text-to-speech synthesizer, but the algorithm will work for any linear model and its associated domain.

UR - http://www.scopus.com/inward/record.url?scp=77953171897&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953171897&partnerID=8YFLogxK

M3 - Conference contribution

VL - Part F129447

SP - 288

EP - 295

BT - Proceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996

PB - Association for Computing Machinery

ER -