  Non-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.
