论文标题
递归贝叶斯分类的停止标准设计：分析和决策几何形状
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry
论文作者
论文摘要
基于递归贝叶斯更新的系统通过某些停止/终止标准限制了证据收集成本，并因此执行决策。通常，基于（i）状态后验分布的最大定义阈值的两个终止标准； （ii）通常使用状态后部不确定性。在本文中，我们提出了对状态后进展的几何解释，因此，我们对使用此类常规终止标准的缺点进行了逐点分析。例如，通过提出的几何解释，我们表明，在状态后代最大的置信阈值中遭受了僵硬，这会导致不必要的证据收集，而基于不确定性的阈值方法对类别数量脆弱并过早地终止，如果某些州候选人已经被发现不利。此外，两种类型的终止方法都忽略了后验更新的演变。然后，我们提出了一个新的停止/终止标准，并具有几何见解，以克服这些常规方法的局限性，并在决策准确性和速度方面进行比较。我们使用模拟并使用通过大脑计算机接口打字系统获得的实际实验数据来验证我们的主张。
Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.
