Absolute convergence and error thresholds in non-active adaptive sampling
- Vilares Ferro, Manuel
- Darriba Bilbao, Víctor M.
- Vilares, Jesús
DOIAbstract
[Abstract] 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 ...
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