2.3.2 “一对多”多类分类算法(One-vs-Rest SVMs) 23-24 2.3.3 基于霍夫曼树(HFMTree)的分类算法 24-27
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With the combination of research achievements in Statistic Learning Theory and Support Vector Machine, this paper proposes a new multi-class classification algorithm based on data description to overcome the drawbacks of current multi-class classification algorithms.
针对已有多类分类算法存在的不足,结合统计学习理论和基于支持向量的数据描述方法的研究成果,提出了基于数据描述的多类分类算法。
参考来源 - 基于支持向量数据描述的多类分类算法及其在人脸识别中的应用A SVM multiclass classification algorithm based on kernel hierarchical clustering is proposed, which not only enhances the speed of training SVM effectively, but also acquires higher precision and speed for classification.
3.提出了一种基于核分级聚类的SVM多类分类算法(KHC-SVM)。 利用核分级聚类算法生成层次结构合理的二叉树,然后利用KNN-SVM构造二叉树各内节点的最优超平面,不仅有效地提高了SVM的训练速度,而且可以进一步提高SVM的分类速度和精度。
参考来源 - 支持向量机学习算法及其在雷达目标识别中的应用·2,447,543篇论文数据,部分数据来源于NoteExpress
给出了一种基于编码二叉树的支持向量的多类分类算法。
Produce an algorithm based on encoding binary tree and supporting vector multi-category classification algorithm.
介绍了支持向量机的变形算法、多类分类算法及模型选择问题;
The transformative algorithm based on SVM, multi-class SVM and model selection are also presented.
第四,利用闭凸包收缩原理和特征空间分离度量方法对决策树多类分类算法进行了改进。
Via abstracting this pre-information, the blindness of choosing the classifier can be reduced and existing method of multi-classification can be advanced to improve their accuracy of classification.
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