Then w β (θ) can be computed as the ratio of marginal negative utility M U F β (T N (θ)) to the marginal positive utility M U F β (T P (θ)), where T P (θ) and T N (θ) are T P and T N on training data with respect to θ-parameterized model: Then at each iteration of the model optimization (i.e., each step of gradient descending), we want the model to take next update step proportional to the gradient of the w β -scaled cross-entropy loss function L AS (θ) at the current point: Consequently, based on the contributions that correctly predicting one more instances of each class bringing to F-measure, the training procedure dynamically adjusts its attention between positive and negative instances.
(2015), a one-layer CNN model with a dynamic multi-pooling operation over convolutional feature maps.