Liu H X, Zhang R S, Luan F, Yao X J, Liu M C, Hu Z D, Fan B T
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
J Chem Inf Comput Sci. 2003 May-Jun;43(3):900-7. doi: 10.1021/ci0256438.
The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.
支持向量机(SVM)分类算法是机器学习领域最近开发出来的,用于诊断乳腺癌。同时,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。分类任务涉及使用从UCI机器学习数据库获得的数据预测疾病状态。总体而言,支持向量机的表现优于k均值聚类和两种人工神经网络。从几种机器学习技术的比较中可以得出结论,有九个样本可能被误标记。