TY - JOUR
T1 - Rejoinder on
T2 - “On active learning methods for manifold data”
AU - Li, Hang
AU - Del Castillo, Enrique
AU - Runger, George
N1 - Publisher Copyright:
© 2019, Sociedad de Estadística e Investigación Operativa.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - We thank the discussants for their comments and careful reading of our manuscript, which have enhanced and complemented our presentation. We also thank the editors of TEST for this opportunity to clarify some aspects of our work in more detail. In what follows, we first address some points touched by both sets of discussants, and then consider comments made individually by each of them. We conclude with a description of a method that can improve the speed of the retraining required in the SSGP-AL method when used for classification by re-using previous learning as opposed to re-estimating the GP model from scratch at each AL cycle.
AB - We thank the discussants for their comments and careful reading of our manuscript, which have enhanced and complemented our presentation. We also thank the editors of TEST for this opportunity to clarify some aspects of our work in more detail. In what follows, we first address some points touched by both sets of discussants, and then consider comments made individually by each of them. We conclude with a description of a method that can improve the speed of the retraining required in the SSGP-AL method when used for classification by re-using previous learning as opposed to re-estimating the GP model from scratch at each AL cycle.
UR - http://www.scopus.com/inward/record.url?scp=85077559063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077559063&partnerID=8YFLogxK
U2 - 10.1007/s11749-019-00697-9
DO - 10.1007/s11749-019-00697-9
M3 - Comment/debate
AN - SCOPUS:85077559063
VL - 29
SP - 42
EP - 49
JO - Trabajos de Estadistica Y de Investigacion Operativa
JF - Trabajos de Estadistica Y de Investigacion Operativa
SN - 0041-0241
IS - 1
ER -