Rejoinder on: “On active learning methods for manifold data”

Hang Li (), Enrique Castillo () and George Runger ()
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Hang Li: The Pennsylvania State University
Enrique Castillo: The Pennsylvania State University
George Runger: Arizona State University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 1, No 4, 42-49

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1007/s11749-019-00697-9

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