Wang Haojun, Zheng Chongxun, Li Ying, Zhu Huafeng, Yan Xiangguo
Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2003 Sep;20(3):484-7.
The support vector machine (SVM) is a new learning technique based on the statistical learning theory. It was originally developed for two-class classification. In this paper, the SVM approach is extended to multi-class classification problems, a hierarchical SVM is applied to classify blood cells in different maturation stages from bone marrow. Based on stepwise decomposition, a hierarchical clustering method is presented to construct the architecture of the hierarchical (tree-like) SVM, then the optimal control parameters of SVM are determined by some criterion for each discriminant step. To verify the performances of classifiers, the SVM method is compared with three classical classifiers using 3-fold cross validation. The preliminary results indicate that the proposed method avoids the curse of dimensionality and has greater generalization. Thus, the method can improve the classification correctness for blood cells from bone marrow.
支持向量机（SVM）是一种基于统计学习理论的新型学习技术。它最初是为二类分类而开发的。本文将支持向量机方法扩展到多类分类问题，应用层次支持向量机对骨髓中不同成熟阶段的血细胞进行分类。基于逐步分解，提出了一种层次聚类方法来构建层次（树状）支持向量机的结构，然后针对每个判别步骤，通过某种准则确定支持向量机的最优控制参数。为了验证分类器的性能，使用3折交叉验证将支持向量机方法与三种经典分类器进行比较。初步结果表明，所提出的方法避免了维数灾难，具有更强的泛化能力。因此，该方法可以提高骨髓血细胞分类的正确性。
