支持向量机及其在地震预报中的应用前景
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摘要
统计学习理论(SLT)是研究小样本情况下机器学习规律的理论。支持向量机(SVM)基于统计学习理论,可以处理高度非线性分类和回归等问题,不但较好地解决了小样本、过学习、高维数、局部最小等实际难题,而且具有很强的泛化(预测)能力。本文介绍了支持向量机的分类、回归方法,分析了这一方法的特点,讨论了该方法在地震预报中的应用前景。
Statistical learning theory(SLT) is a small-sample statistics theory.Support vector machine(SVM) is a new machine learning method based on statistical learning theory.It can process the high nonlinear problems with classification and regression.SVM not only can solve some problems,such as small-sample,over-fitting,high-dimension and local minimum,but also has higher generalization(forecasting) ability than that of the artificial neural networks.In this paper,the classification and regression methods of SVM are introduced,the characters of the methods are analyzed,and the application future of SVM in earthquake prediction is discussed also.
Statistical learning theory(SLT) is a small-sample statistics theory.Support vector machine(SVM) is a new machine learning method based on statistical learning theory.It can process the high nonlinear problems with classification and regression.SVM not only can solve some problems,such as small-sample,over-fitting,high-dimension and local minimum,but also has higher generalization(forecasting) ability than that of the artificial neural networks.In this paper,the classification and regression methods of SVM are introduced,the characters of the methods are analyzed,and the application future of SVM in earthquake prediction is discussed also.
引文
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[3]Chen N ian-Y i,Lu W en-Cong,Yang Jie,et al..Support vectormach ines in Chem istry[M].S ingaore:W orld Scientific Pub lish ingCompany,2004.
[4]崔万照,朱长纯,保文信星,等.混沌时间序列的支持向量机预测[J].物理学报,2004,53(10):3304-3309.
[5]Mukherjee S,Osuma E,G irosi F.Non linear Pred iction of ChaoticTim e Series Using SupportVectorMach ines[J].Neural Networksfor S ignal ProcessingⅦ,1997:511-520.
[6]孙德山,吴今培,侯振挺,等.基于SVR的混沌时间序列预测[J].计算机工程应用,2004,54(2):54-56.
[7]陈永义,俞小鼎,高学浩,等.处理非线性分类和回归问题的一种新方法(I)———支持向量机方法简介[J].应用气象学报,2004,15(3):345-354.
[8]王国胜,钟义信.支持向量机的若干新进展[J].电子学报,2001,29(10):1397-1400.
[9]陈念贻,陆文聪,叶晨周,等.支持向量机及其他核函数算法在化学计量学中的应用[J].计算机与应用化学,2002,19(6):82-106.
[10]祁亨年.支持向量机及其应用研究综述[J].计算机工程,2004,30(10):6-10.
[11]王炜,曹雪峰,宋先月.使用人工神经网络判断未来我国地震形势[J].地震学刊,2001,3(21):10-14.
[12]王炜,谢端,宋先月,等.使用人工神经网络进行我国大陆强震时间序列预测[J].西北地震学报,2002,24(4):315-320.
[2]C ristian in i N,Shawe-Taylor J著.李国正,等译.支持向量机导论[M].北京:电子工业出版社,2004.82-106.
[3]Chen N ian-Y i,Lu W en-Cong,Yang Jie,et al..Support vectormach ines in Chem istry[M].S ingaore:W orld Scientific Pub lish ingCompany,2004.
[4]崔万照,朱长纯,保文信星,等.混沌时间序列的支持向量机预测[J].物理学报,2004,53(10):3304-3309.
[5]Mukherjee S,Osuma E,G irosi F.Non linear Pred iction of ChaoticTim e Series Using SupportVectorMach ines[J].Neural Networksfor S ignal ProcessingⅦ,1997:511-520.
[6]孙德山,吴今培,侯振挺,等.基于SVR的混沌时间序列预测[J].计算机工程应用,2004,54(2):54-56.
[7]陈永义,俞小鼎,高学浩,等.处理非线性分类和回归问题的一种新方法(I)———支持向量机方法简介[J].应用气象学报,2004,15(3):345-354.
[8]王国胜,钟义信.支持向量机的若干新进展[J].电子学报,2001,29(10):1397-1400.
[9]陈念贻,陆文聪,叶晨周,等.支持向量机及其他核函数算法在化学计量学中的应用[J].计算机与应用化学,2002,19(6):82-106.
[10]祁亨年.支持向量机及其应用研究综述[J].计算机工程,2004,30(10):6-10.
[11]王炜,曹雪峰,宋先月.使用人工神经网络判断未来我国地震形势[J].地震学刊,2001,3(21):10-14.
[12]王炜,谢端,宋先月,等.使用人工神经网络进行我国大陆强震时间序列预测[J].西北地震学报,2002,24(4):315-320.
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