论文标题
卷积支撑向量机
Convolutional Support Vector Machine
论文作者
论文摘要
支持向量机（SVM）和深度学习（例如，卷积神经网络（CNNS））分别是小数据和大数据中最著名的两种算法。尽管如此，较小的数据集可能非常重要，昂贵且在短时间内不容易获得。本文提出了一种新型的卷积SVM（CSVM），既具有CNN和SVM的优势，又可以提高采矿较小数据集的准确性和有效性。拟议的CSVM从CNN适应了卷积产品，以学习隐藏在数据集中的新信息。此外，它使用修改的简化群体优化（SSO）来帮助训练CSVM以更新分类器，然后将传统的SVM实现为SSO的适合度，以估计准确性。为了评估所提出的CSVM的性能，进行了实验，以测试五个众所周知的基准数据库，以解决分类问题。数值实验与使用SVM，3层人工NN（ANN）和4层ANN获得的数值实验。这些实验的结果验证了使用拟议的SSO提出的CSVM可以有效提高分类精度。
The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has both advantages of CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.
