Absolute convergence and error thresholds in non-active adaptive sampling

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGNon-active adaptive sampling is a way of building machine learning models from a training data base which are supposed to dynamically and automatically derive guaranteed sample size. In this context and regardless of the strategy used in both scheduling and generating of weak predictors, a proposal for calculating absolute convergence and error thresholds is described. We not only make it possible to establish when the quality of the model no longer increases, but also supplies a proximity condition to estimate in absolute terms how close it is to achieving such a goal, thus supporting decision making for fine-tuning learning parameters in model selection. The technique proves its correctness and completeness with respect to our working hypotheses, in addition to strengthening the robustness of the sampling scheme. Tests meet our expectations and illustrate the proposal in the domain of natural language processing, taking the generation of part-of-speech taggers as case study.Agencia Estatal de Investigación | Ref. TIN2017-85160-C2-1-RAgencia Estatal de Investigación | Ref. TIN2017-85160-C2-2-RAgencia Estatal de Investigación | PID2020-113230RB-C21Agencia Estatal de Investigación | PID2020-113230RB-C22Xunta de Galicia | Ref. ED431C 2018/5