TY - GEN

T1 - Selecting training inputs via greedy rank covering

AU - Buchsbaum, Adam L.

AU - Van Santen, Jan P.H.

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

AN - SCOPUS:77953171897

T3 - Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

SP - 288

EP - 295

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

PB - Association for Computing Machinery

T2 - 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996

Y2 - 28 January 1996 through 30 January 1996

ER -