支持向量机中优化算法

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摘要：Optimization algorithm solving Lagrangian multipliers is the key of training SVM,determining the perfor-mance of SVM ,affecting practical applications of SVM in various fields widely. Some kinds of optimization algorithmsin SVM of overseas are introduced. We classify the optimization algorithms into two kinds: 1. the algorithms based onOsuna's decomposition strategy; 2. The iterative algorithms based on the changes of SVM formulation proposed byO. L. Mangasarian. We also analyze the characteristics of various optimization algorithms in SVM ,and predicting thetrend of research on optimization algorithm in SVM.

原文链接:http://www.cqvip.com//QK/92817X/200303/7788426.html

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关键词：支持向量机 向量机 Optimization Applications On Strategy 支持向量机 优化算法 机器学习 数据分类 可信模型