Ensemble methods have shown to improve the results of statistical classifiers by combining multiple single learners into a strong one. In this paper, we explore the use of ensemble methods at the level of the objective function of a deep neural network. We propose a novel objective function that is a linear combination of single losses and integrate the proposed objective function into a deep neural network. By doing so, the weights associated with the linear combination of losses are learned by back propagation during the training stage. We study the impact of such an ensemble loss function on the state-of-the-art convolutional neural networks for text classification. We show the effectiveness of our approach through comprehensive experiments on text classification. The experimental results demonstrate a significant improvement compared with the conventional state-of-the-art methods in the literature.

中文翻译：

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组合损失函数以进行深层文本分类

集成方法显示出可以通过将多个单一学习者组合为一个强大的学习者来改善统计分类器的结果。在本文中，我们在深度神经网络的目标函数级别上探索了集成方法的使用。我们提出了一种新颖的目标函数，该函数是单个损失的线性组合，并将提出的目标函数集成到一个深度神经网络中。通过这样做，在训练阶段通过反向传播来学习与损失的线性组合相关的权重。我们研究了这种整体损失函数对用于文本分类的最新卷积神经网络的影响。我们通过对文本分类的综合实验来证明我们的方法的有效性。