On Extending Neural Networks with Loss Ensembles for Text Classification
Table 3: Accuracy
['[BOLD] Dataset', '[BOLD] Cross-entropy', '[BOLD] Hinge', '[BOLD] Square', '[BOLD] Ensemble']
Since this work is a proof of concept, the neural networks of each application are simply a softmax of the linear combination of input features plus bias: ^y=softmax(x⋅W+b) where the input features x are the word frequencies in the input text. Thus, θ in our notation is composed of W and b. We use Python and its TensorFlow package for implementing the proposed approach. The table compares the results of using individual loss functions and the ensemble loss.