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 -