摘要
本文针对两种不同用途的支撑矢量机,分类支撑矢量机和回归支撑矢量机,分别证明了它们的一些几何性质,从这些性质出发讨论了这两种支撑矢量机对新增样本的推广能力,新增样本对支撑矢量,非支撑矢量的影响以及新增样本本身的一些特点,得到了一些非常有价值的结论.从这些结论可以看出支撑矢量机对新增样本具有良好的推广能力,即对新增样本的良好的包容性和适应性,并且支撑矢量机是一种可积累的学习模型.
Abstract
Some geometry of Support Vector Machines for classification and regression is described and proven.And then the generalization performance of SVMs on newly-added samples is discussed.Through the analysis of the property of newly-added samples and the effect of them on support vectors and non-support vectors,some valuable results are presented.These enable us to conclude that SVM has a good compatibility,adaptability and generalization performance for newly-added samples and is a hereditable learning model.
关键词
分类支撑矢量机 /
回归支撑矢量机 /
学习机 /
KKT条件 /
可积累性
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Key words
support vector classification /
support vector regression /
learning machines /
KKT conditions /
hereditability
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周伟达;张 莉;焦李成.
支撑矢量机推广能力分析[J]. 电子学报, 2001, 29(5): 590-594.
ZHOU Wei-da;ZHANG Li;JIAO Li-cheng.
An Analysis of SVMs Generalization Performance[J]. Acta Electronica Sinica, 2001, 29(5): 590-594.
中图分类号:
O 23.5
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脚注
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基金
国家"863"项目 (No.863-306-06-06-1)和教育部博士点基金
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PDF(192 KB)
国家"863"项目(No.863-306-06-06-1)和教育部博士点基金