Short text classification method based on deep neural mapping support vector machine

The invention discloses a short text classification method based on a deep neural mapping support vector machine, and belongs to the field of text classification and deep learning. A short text classification algorithm combining a convolutional neural network and the deep neural mapping support vector machine (DNMSVM) is provided for solving the problems that Softmax is adopted as a classifier forthe convolutional neural network, the generalization capability is insufficient, feature extraction and kernel function learning are needed for directly using the support vector machine for classification and the optimal solution is often difficult to achieve, and thus the classification effect on short text is improved. By means of the method, complex preprocessing of the text is not needed, theaccuracy is high, and the reliability and robustness are improved. 本发明公开了种基于深度神经映射支持向量机的短文本分类方法，属于文本分类与深度学习领域。采用Softmax作为卷积神经网络的分类器导致泛化能力不足，而直接使用支持向量机分类需要进行特征提取和核函数的学习，往往难以达到最优解，于是提出了种结合卷积神经网络和支持向量机(DNMSVM，深度神经映射支持向量机)的短文本分类算