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

Hang Li, Enrique Del Castillo, George Runger

Research output: Contribution to journalComment/debate

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.

Original languageEnglish (US)
Pages (from-to)42-49
Number of pages8
JournalTest
Volume29
Issue number1
DOIs
StatePublished - Mar 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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