RT Journal Article
T1 Absolute convergence and error thresholds in non-active adaptive sampling
A1 Vilares Ferro, Manuel
A1 Darriba Bilbao, Victor Manuel
A1 Vilares Ferro, Jesús
K1 3304.11 Diseño de Sistemas de Cálculo
AB 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.
PB Journal of Computer and System Sciences
SN 00220000
YR 2022
FD 2022-11
LK http://hdl.handle.net/11093/3496
UL http://hdl.handle.net/11093/3496
LA eng
NO Journal of Computer and System Sciences, 129, 39-61 (2022)
NO Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG
NO Agencia Estatal de Investigación | Ref. TIN2017-85160-C2-1-R
DS Investigo
RD 09-sep-2024