### 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 |

ISBN (Electronic) | 0898713668 |

State | Published - Jan 28 1996 |

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

### Publication series

Name | Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms |
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Volume | Part F129447 |

### 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 |

### ASJC Scopus subject areas

- Software
- Mathematics(all)

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## Cite this

Buchsbaum, A. L., & Van Santen, J. P. H. (1996). Selecting training inputs via greedy rank covering. In

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