Burbidge R, Trotter M, Buxton B, Holden S
University College London, Gower Street, London WCIE 6BT, UK.
Comput Chem. 2001 Dec;26(1):5-14. doi: 10.1016/s0097-8485(01)00094-8.
We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.
我们证明，支持向量机（SVM）分类算法作为机器学习领域的一项最新进展，在构效关系分析中展现出了潜力。在一项基准测试中，将支持向量机与该领域目前使用的几种机器学习技术进行了比较。分类任务是利用从UCI机器学习数据库获得的数据，预测嘧啶对二氢叶酸还原酶的抑制作用。支持向量机的表现优于三个神经网络、一个径向基函数网络和一个C5.0决策树。除了一个人工控制容量的神经网络外，支持向量机明显优于所有这些技术，而该神经网络的训练时间要长得多。
