Fig. 2.

Adaptive training methodologies for the coefficients in the Generalized loss function. (a) Exploratory model where all possible actions (−10%, no change, +10%) are explored at each iteration step for each of the three parameters, and the action triplet yielding the best performance $(\underset{A_j}{argmin}{\mathcal{L}}_{Aj}^{i+1})$ is picked. (b) Deterministic model based on rule-based update formula for the three coefficients that can each take one of five possible actions. For example, α_P decreases by a factor of 50% if TP/FP is smaller than 1/15. In order to avoid the unbounded divergence of the parameters, the parameters α_P and α_N are not updated when <FP> ≤5 and <FN> ≤ 1, respectively, where the symbols <·> denote batch-averaged values.