This paper proposes a weighted C-SVM algorithm and analyzes its classification performance theoretically.
提出了一种加权C—SVM分类算法，并从理论上分析了算法的性能。
Time series modeling and identification techniques were analyzed and the ARMA time series model based on robust LS-SVM algorithm was proposed.
对时序数据建模与辨识技术进行了分析，提出了使用鲁棒LS-SVM算法建立ARMA时序预测模型。
Experiments are made on UCI database, and the results indicate the MBI-SVM algorithm has many good characteristics, such as stability, feasibility and practicality.
在UCI数据库上进行了实验，结果证实该算法具有较高的稳定性、可行性和实用性。
A method for pre-extracting support vectors based on concentric hyperspheres division is presented in this paper, and HD-SVM algorithm based on this method is presented also.
提出了一个基于同心超球面分割的支持向量预抽取方法，并在此基础上给出了HD - SVM训练算法。
Based on the structural risk minimization principle, the latest data mining method, support vector machine (SVM) algorithm, in artificial intelligence field was introduced in this paper.
介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。
Combined with support vector machine (SVM) algorithm, several common approaches to deal with the unbalanced problem were compared, including majority voting, the moving boundary surface, etc.
针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。
Combined with support vector machine (SVM) algorithm, several common approaches to deal with the unbalanced problem were compared, including majority voting, the moving boundary surface, etc.
针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。
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