  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.
