Deep Non-Parallel Hyperplane Support Vector Machine for Classification

In the last few decades, deep learning based on neural networks has become popular for the classification tasks, which combines feature extraction with the classification tasks and always achieves the satisfactory performance. Non-parallel hyperplane support vector machine (NPHSVM) aims at constructing two non-parallel hyperplanes to classify data and extracted features are always used to be input data for NPHSVM. As for NPHSVM, extracted features will greatly influence the performance of the model to some extent. Therefore, in this paper, we propose a novel DNHSVM for classification, which combines deep feature extraction with the generation of hyperplanes seamlessly. Each hyperplane is close to its own class and as far as possible to other classes, and deep features are friendly for classification and samples are easy to be classified. Experiments on UCI datasets show the effectiveness of our proposed method, which outperforms other compared state-of-the-art algorithms.