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计算海量数据支持向量的几何方法
丁辉
Subtype工学硕士
Thesis Advisor王珏
2001-05-01
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword机器学习 统计学习理论 支持向量机 海量数据 几何方法 邻域原理 Machine Learning Statistical Learning Theory Support Vector Machine Massive Data Geometrical Method Neighborhood Principle
Abstract本论文首先从几何的角度探究了机器学习的历史。从感知机模 型的提出,到神经网络的研究热潮,到Marvin Minsky对神经网络 的批评,最后到Vapnik的统计学习理论和支持向量机,都进行了充 分的论述和几何意义的探究。 而后,我们提出了求解海量数据支持向量的基于邻域原理的复 合核函数的几何方法,并给出了相应的理论证明和几何解释,以求 能够消除以往神经网络学习中没有任何几何意义的致命弱点,并能 够将对支持向量机的研究从二次规划的路上重新引导回几何方法的 研究上。 最后,给出了求解支持向量的几何方法的试验结果,并且简单 指出了几何方法所存在的问题。
Other AbstractIn this paper, we first investigate the history of machine learning in a geometrical view. From the concept of perceptron, then the research upsurge of artificial neural networks, then Marvin Minsky's criticism on neural networks, until Vapnik's statistical learning theory and support vector machine, all of their geometrical senses have been formulated and investigated in detail. Then, we advance the geometrical method of compound kernel function based upon neighborhood principle to compute the support vectors of massive data. Corresponding theoretical proof and geometrical explanation are also presented. The purpose of our work is to eliminate the fatal weakness of artificial neural networks, i.e., having not any geometrical sense. We also wish to redirect the research focus on SVM from quadratic programming to geometrical methods. In the last chapter, we give the experimental results using the geometrical method to compute the support vectors. The problems to be conquered in the future is also given briefly.
shelfnumXWLW591
Other Identifier591
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7326
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
丁辉. 计算海量数据支持向量的几何方法[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2001.
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