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最小二乘支持向量机的两点改进

DOI: 10.3969/j.issn.1006-7043.201404078

Keywords: 高光谱|支持向量机|样本缩减|马尔科夫随机场|空间信息

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Abstract:

最小二乘支持向量机以其较好的性能得到了广泛应用, 但仍存在2点不足:一方面, 最小二乘支持向量机将所有训练样本都作为支持向量参与未知样本的分类, 导致该算法在泛化过程中处理速度较慢;另一方面, 最小二乘支持向量机主要利用光谱数据进行训练和分类, 忽略了对地物空间信息的挖掘, 影响了分类精度。为此, 提出一种基于库伦引力模型的样本缩减策略, 在此基础上将分类结果与基于空间信息的分类器相融合, 由此产生的新分类器可以有效解决以上两方面的问题。实验表明了新分类模型在分类精度与速度方面的优势。

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