| 边界K邻近大样本支持向量机分类 |
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| 引用本文： | 奉国和.边界K邻近大样本支持向量机分类[J].计算机工程与应用,2009,45(23):15-17. |
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| 作者姓名： | 奉国和 |
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| 作者单位： | 华南师范大学经济管理学院信息管理系,广州,510006 |
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| 基金项目： | 国家社会科学基金项目，广东省哲学社会科学规划项目 |
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| 摘 要： | 针对大样本支持向量机内存开销大、训练速度慢的缺点,提出了一种改进的支持向量机算法。算法先利用KNN方法找出可能支持向量,然后利用SVM在可能支持向量集上训练得到分类器。实验表明改进算法训练速度提高明显。
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| 关 键 词： | 支持向量机 大样本 分类 |
| 收稿时间： | 2009-5-6 |
| 修稿时间： | 2009-6-7
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Research on large scale SVM classification based on boundary K-nearest |
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| FENG Guo-he.Research on large scale SVM classification based on boundary K-nearest[J].Computer Engineering and Applications,2009,45(23):15-17. |
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| Authors: | FENG Guo-he |
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| Affiliation: | FENG Guo-he College of Economics , Management,South China Normal University,Guangzhou 510006,China |
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| Abstract: | The problem of occupying much memory and slow training speed will come forth for Support Vector Machine(SVM) with large scale training set.This paper puts forward a boundary K-NN SVM algorithm,searching for possible support vectors with K-NN and training SVM classifier based on such support vectors.Experiment shows that modified algorithm training speed is advanced. |
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| Keywords: | upport Vector Machine（SVM） large-scale samples classification |
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