| 一种支持向量分类学习算法 |
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| 引用本文： | 李静,彭江涛.一种支持向量分类学习算法[J].湖北大学学报(自然科学版),2009,31(2):122-124. |
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| 作者姓名： | 李静 彭江涛 |
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| 作者单位： | 湖北大学数学与计算机科学学院,湖北,武汉,430062
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| 摘 要： | 分析样本与KKT条件之间的关系以及新增一个样本点对原支持向量分类学习结果的影响，并针对支持向量机在较多训练样本时需要较大的计算量和内存的问题，基于已有的算法提出了一种新的学习算法．实现了对样本集的有效压缩，有利于运算速度的改善．
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| 关 键 词： | 支持向量机 学习算法 决策函数 |
A support vector machine learning algorithm |
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| LI Jing,PENG Jiang-tao.A support vector machine learning algorithm[J].Journal of Hubei University(Natural Science Edition),2009,31(2):122-124. |
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| Authors: | LI Jing PENG Jiang-tao |
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| Institution: | School of Mathematics and Computer Science;Hubei University;Wuhan 430062;China |
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| Abstract: | This paper analysised the relationships between training set and KKT condition,and the possible change of support vector learning after adding a new sample to training set.The SVM algorithm may need more calculation and memory.When we had too many samples,based on the old learning algorithm,we presented a support vector machine learning algorithm.It can compress the training set effectively and improve computational speed. |
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| Keywords: | support vector machine learning algorithm decision function |
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