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计算机工程 ›› 2009, Vol. 35 ›› Issue (14): 236-237. doi: 10.3969/j.issn.1000-3428.2009.14.082

• 人工智能及识别技术 • 上一篇    下一篇

改进的模糊最小二乘支持向量机模型

许 亮   

  1. (广东工业大学自动化学院,广州 510006)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-07-20 发布日期:2009-07-20

Improved Fuzzy Least Squares Support Vector Machines Model

XU Liang   

  1. (School of Automation, Guangdong University of Technology, Guangzhou 510006)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-07-20 Published:2009-07-20

摘要: 针对最小二乘支持向量机对噪声或孤立点敏感的问题,提出一种融合先验知识的模糊最小二乘支持向量机模型。在训练过程中考虑样本的噪声分布模型,结合样本紧密度策略,自动生成相应样本的模糊隶属度。实验结果表明,该模型对噪声样本具有较好的分类精度。

关键词: 最小二乘支持向量机, 模糊隶属度, 噪声分布模型

Abstract: Aiming at the problem that the Least Squares Support Vector Machines(LSSVM) is sensitive to noises or outliers, a LSSVM model incorporating with a prior knowledge on data is proposed. Information of noise distribution for samples is introduced in the training process. A strategy based on the sample affinity is presented to discriminate data with noises. A fuzzy membership is automatically generated and assigned to each corresponding data point in the sample set by using the strategy and the noise model. Experimental result shows that the proposed model has better classification accuracy with noise data.

Key words: Least Squares Support Vector Machines(LSSVM), fuzzy membership, noise distribution model

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