| 核向量机算法研究及应用 |
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| 引用本文： | 许敏. 核向量机算法研究及应用[J]. 无锡职业技术学院学报, 2012, 11(4): 73-76 |
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| 作者姓名： | 许敏 |
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| 作者单位： | 无锡职业技术学院电子与信息技术学院,江苏无锡,214121 |
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| 摘 要： | 对训练样本规模为m的标准支持向量机(Support Vector Machine,SVM)进行训练,时间复杂度为O(m3),空间复杂度为O(m2)。文章研究将其转换成等价的最小包含球(Minimum Enclosing Ball,MEB)形式,使用核心集向量机(Core Vector Machine,CVM)高效获得近似最优解。CVM的优点是时间复杂度与训练样本规模m呈线性关系,空间复杂度与m无关。实验证明,CVM可以对大规模数据集进行高效的分类。
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| 关 键 词： | 核向量机 支持向量机 最小包含球 核函数 |
Core Vector Machine Algorithm and its Application |
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| XU Min. Core Vector Machine Algorithm and its Application[J]. , 2012, 11(4): 73-76 |
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| Authors: | XU Min |
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| Affiliation: | XU Min(School of Electronic and Information Technology,Wuxi Institute of Technology,Wuxi 214121,China) |
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| Abstract: | According to the training set size m,standard SVM training has O（m3） time and O（m2） space complexities.In this paper,CVM algorithm is discussed.It transforms the kernel method into equivalent MEB problems,and gets the approximately optimal solutions efficiently by core set.CVM has a time complexity that is liner in m and a space complexity that is independent of m.The result shows that CVM algorithm can handle much larger data sets than existing scale up methods. |
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| Keywords: | Core Vector Machine(CVM) Support Vector Machine(SVM) Minimum Enclosing Ball(MEB) kernel function |
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