计算机科学 ›› 2014, Vol. 41 ›› Issue (5): 245-249.doi: 10.11896/j.issn.1002-137X.2014.05.052
胡文军,王娟,王培良,王士同
HU Wen-jun,WANG Juan,WANG Pei-liang and WANG Shi-tong
摘要： 线性SVM具有算法简单、训练和测试速度快等优点,但不能用于解决线性不可分问题。为此,将样本数据集划分为多个集合并分别构造它们的LSVM,然后运用径向基函数的非线性组合来拟合非线性的决策函数,从而解决线性不可分问题。鉴于此,提出了一种适合非线性大样本分类的LSVM快速集成模型FMELSVM。该模型利用径向基函数RBF改善了LSVM的非线性输出能力,同时引进了优化权来提升LSVM的集成效果。UCI数据集的实验结果表明,FMELSVM在处理大样本方面具有较好的性能优势。
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