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.

基于部分结果和功能策略，引入了一种算法来估计整个训练数据库中学习曲线的演变，迭代逼近所需时间的值，独立于学习技术的使用，并且在经过预测级别后，被证明在工作假设方面是正确的，使用户能够根据最终可达到的准确性设定收敛阈值的概念扩展，即使存在扭曲观测也似乎是有效的。我们的目标是评估培训工作量，支持决策制定，以减少在学习过程中对人力和计算资源的需求。该提案在至少三个操作过程中是有意义的，第一个是预测准确性的提升，以衡量达到一定性能水平所需的工作量，第二个是在训练时间内比较系统的效率，以仅完成最适合我们要求的系统的任务，同时准确性的预测也是定制系统的有价值信息，因为我们可以预先估计设置对性能和开发成本的影响。通过生成词性标注器作为示例应用程序，实验结果与我们的预期一致。