Modeling of learning curves with applications to pos tagging
<|reference_start|>Modeling of learning curves with applications to pos tagging: An algorithm to estimate the evolution of learning curves on the whole of a training data base, based on the results obtained from a portion and using a functional strategy, is introduced. We approximate iteratively the sought value at the desired time, independently of the learning technique used and once a point in the process, called prediction level, has been passed. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition. This allows the user to fix a convergence threshold with respect to the accuracy finally achievable, which extends the concept of stopping criterion and seems to be effective even in the presence of distorting observations.   Our aim is to evaluate the training effort, supporting decision making in order to reduce the need for both human and computational resources during the learning process. The proposal is of interest in at least three operational procedures. The first is the anticipation of accuracy gain, with the purpose of measuring how much work is needed to achieve a certain degree of performance. The second relates the comparison of efficiency between systems at training time, with the objective of completing this task only for the one that best suits our requirements. The prediction of accuracy is also a valuable item of information for customizing systems, since we can estimate in advance the impact of settings on both the performance and the development costs. Using the generation of part-of-speech taggers as an example application, the experimental results are consistent with our expectations.<|reference_end|>
@article{ferro2024modeling,
    title={Modeling of learning curves with applications to pos tagging},
    author={Manuel Vilares Ferro, Victor M. Darriba Bilbao, Francisco J. Ribadas
  Pena},
    journal={Computer Speech & Language, 41, pp 1-28 (2017). ISSN 0885-2308.
  Elsevier},
    year={2024},
    doi={10.1016/j.csl.2016.06.001},
    archivePrefix={arXiv},
    eprint={2402.02515},
    primaryClass={cs.CL cs.AI cs.LG}
}