Selecting training inputs via greedy rank covering
Conference
·
OSTI ID:416811
- AT&T Bell Laboratories, Murray Hill, NJ (United States)
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.
- OSTI ID:
- 416811
- Report Number(s):
- CONF-960121-; TRN: 96:005887-0034
- Resource Relation:
- Conference: 7. annual ACM-SIAM symposium on discrete algorithms, Atlanta, GA (United States), 28-30 Jan 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the seventh annual ACM-SIAM symposium on discrete algorithms; PB: 596 p.
- Country of Publication:
- United States
- Language:
- English
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