基于支持向量机的改进分类算法

Improved Classification Algorithm Based on Support Vector Machine

为了进一步提高支持向量机分类的准确性和泛化能力,提出一种基于支持向量机的改进二叉树分类算法.首先介绍支持向量机的基本原理,总结了常见的多分类器分类算法及其特点,结合现有分类算法的优点,为分类器引入了不同的权值,提出二叉树改进分类算法,有效避免了常用分类算法不足.通过仿真实验,与典型的多类分类算法对比,验证该算法的有效性,为多类分类预测研究提供了一条有效的途径.

In order to improve the accuracy and generalization ability of Support Vector Machine (SVM) classification, this paper presents an improved binary tree classification algorithm based on SVM. It introduces basic principle of SVM, and summarizes multi-classifier classification algorithm and characteristics. Combining the advantages of the classification algorithms and introducing different weights for the classifier, this study proposes improved classification algorithm of the binary tree, which effectively avoids the shortage of common classification algorithms. Simulation experiments and comparison of the typical multi-class classification algorithms verify that the algorithm is effective. The algorithm provides an effective way for multi-class classification prediction research.