  We introduce an adaptive scheduling for adaptive sampling as a novel way of
machine learning in the construction of part-of-speech taggers. The goal is to
speed up the training on large data sets, without significant loss of
performance with regard to an optimal configuration. In contrast to previous
methods using a random, fixed or regularly rising spacing between the
instances, ours analyzes the shape of the learning curve geometrically in
conjunction with a functional model to increase or decrease it at any time. The
algorithm proves to be formally correct regarding our working hypotheses.
Namely, given a case, the following one is the nearest ensuring a net gain of
learning ability from the former, it being possible to modulate the level of
requirement for this condition. We also improve the robustness of sampling by
paying greater attention to those regions of the training data base subject to
a temporary inflation in performance, thus preventing the learning from
stopping prematurely.
  The proposal has been evaluated on the basis of its reliability to identify
the convergence of models, corroborating our expectations. While a concrete
halting condition is used for testing, users can choose any condition
whatsoever to suit their own specific needs.
