非主动自适应采样中的绝对收敛与误差阈值
Absolute convergence and error thresholds in non-active adaptive sampling
AI总结：
本文提出一种计算绝对收敛和误差阈值的方法，用于非主动自适应采样，以确定模型质量何时停止提升并估计其接近程度，通过词性标注器生成实验验证了其正确性和鲁棒性。
摘要：
非主动自适应采样是一种从训练数据库构建机器学习模型的方法，该方法旨在动态且自动地推导出保证的样本量。在此背景下，无论采用何种策略来调度和生成弱预测器，本文都描述了一种计算绝对收敛和误差阈值的方案。我们不仅能够确定模型质量何时不再提升，还提供了一种邻近条件，以绝对量估计其距达成该目标还有多远，从而支持在模型选择中微调学习参数的决策。该技术相对于我们的工作假设证明了其正确性和完备性，并增强了采样方案的鲁棒性。测试符合我们的预期，并在自然语言处理领域以生成词性标注器作为案例研究展示了该方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
