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| 最小二乘模糊支撑向量机研究 | |
| 引用本文： | 何敏藩. 最小二乘模糊支撑向量机研究[J]. 佛山科学技术学院学报(自然科学版), 2008, 26(4) |
| 作者姓名： | 何敏藩 |
| 作者单位： | 佛山科学技术学院,信息科学与数学系,广东,佛山,528000 |
| 摘 要： | 支撑向量机是以统计学习理论为基础，以结构风险最小化（Structure Risk Minimization，SRM）为原则的新型学习机，已经广泛地用于模式识别、回归估计、函数逼近、密度估计等方面。在对已有的分类问题的SVM算法的研究分析基础上，结合Lin和Wang提出的模糊支撑向量机模型和现有的最小二乘支撑向量机模型得出最小二乘模糊支撑向量机模型。 |
| 关 键 词： | 支撑向量机 模糊支撑向量机 最小二乘模糊支撑向量机 |
Research of fuzzy least squares support vector machines |
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| HE Min-fan. Research of fuzzy least squares support vector machines[J]. Journal of Foshan University(Natural Science Edition), 2008, 26(4) | |
| Authors: | HE Min-fan |
| Abstract: | SVM is a new learning machine based on the Statistical Learning Theory of V.Vapnik and its decision-making principle is Structure Risk Minimization(SRM).SVM has been widely used for Pattern Recognition,Regression Estimation,Function Approximation,Density Estimation,etc.In this paper, enlightened by Lin and Wang's Fuzzy SVM,we proposed the fuzzy least squares support vector machines. |
| Keywords: | support vector machine fuzzy support vector machine fuzzy least squares support vector machine |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |