李冬琴, 王丽铮, 王呈方. 支持向量机回归方法在船型要素建模中的应用[J]. 中国舰船研究, 2007, 2(3): 18-21,39. DOI: 10.3969/j.issn.1673-3185.2007.03.004
引用本文: 李冬琴, 王丽铮, 王呈方. 支持向量机回归方法在船型要素建模中的应用[J]. 中国舰船研究, 2007, 2(3): 18-21,39. DOI: 10.3969/j.issn.1673-3185.2007.03.004
Li Dongqin, Wang Lizheng, Wang Chengfang. Method of Support Vector Regression in Modeling Ship Principal Particulars[J]. Chinese Journal of Ship Research, 2007, 2(3): 18-21,39. DOI: 10.3969/j.issn.1673-3185.2007.03.004
Citation: Li Dongqin, Wang Lizheng, Wang Chengfang. Method of Support Vector Regression in Modeling Ship Principal Particulars[J]. Chinese Journal of Ship Research, 2007, 2(3): 18-21,39. DOI: 10.3969/j.issn.1673-3185.2007.03.004

支持向量机回归方法在船型要素建模中的应用

Method of Support Vector Regression in Modeling Ship Principal Particulars

  • 摘要: 支持向量机是基于统计学习理论框架下的一种新的通用机器学习方法,是一种处理非线性分类和非线性回归的有效方法。采用支持向量机回归算法对船型主要要素进行建模,并与常规的回归建模方法进行比较。同时应用实例进行论证,估算结果证明了这种支持向量机回归算法在船型要素建模预测中的有效性和实用性。

     

    Abstract: The focus of this paper is on the ship's principal particulars modeling with Support Vector Machine (SVM). The SVM is a new general learning method based on the statistic learning system which can be used as an effective means to process the non-linear classification and regression. The principal particulars of ships were modeled using Support Vector Regression (SVR) , and the results were compared with those obtained by ordiinary methods. The prediction results demonstrate that it is practicable and effective in the modeling of ship's principal particulars.

     

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