计算机科学 ›› 2016, Vol. 43 ›› Issue (5): 252-256.doi: 10.11896/j.issn.1002-137X.2016.05.047
邢红杰,魏勇乐
XING Hong-jie and WEI Yong-le
摘要： 提出基于信息理论学习中相关熵和距离方差的支持向量数据描述选择性集成。利用相关熵代替均方误差来度量集成的紧致性,构造出更为紧致的分类边界；利用距离方差集成度量集成中基分类器间的差异性,以提高集成模型的差异性；在目标函数中增加基于1范数的正则化项,实现选择性集成。此外,利用半二次优化技术对所提选择性集成模型进行求解。与单个支持向量数据描述、基于Bagging的支持向量数据描述集成以及基于AdaBoost的支持向量数据描述集成相比,所提方法取得了更优的分类性能。
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