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.

中文翻译：

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递归贝叶斯分类的停止准则设计：分析和决策几何


基于递归贝叶斯分类更新的系统通过某些停止/终止标准限制证据收集的成本，并相应地强制执行决策。传统上，两个终止标准基于预定义的阈值（i）状态后验分布的最大值； (ii) 状态后验不确定性是常用的。在本文中，我们提出了对状态后验进展的几何解释，并相应地对使用这种传统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，在最大状态后验上定义的置信阈值会受到刚性的影响，从而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很脆弱，并且如果已经发现了一些候选状态，则会过早终止是不利的。此外，两种类型的终止方法都忽略了后验更新的演化。然后，我们提出了一种具有几何洞察力的新停止/终止标准，以克服这些传统方法的局限性，并提供决策准确性和速度方面的比较。我们使用模拟和通过脑机接口打字系统获得的真实实验数据来验证我们的主张。