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- W2317395801 abstract "摘要: 该文针对支持向量机(SVM)及其变种的不足，提出一种基于熵理论和核密度估计的最大间隔学习机MLMEK。MLMEK引入了核密度估计和熵的概念，用核密度估计表征样本数据的分布特征，用熵表征分类的不确定性。MLMEK真实反映样本数据的分布特征；同时解决两类分类问题和单类分类问题；比传统SVM具有更好的分类性能。UCI数据集上的实验验证了MLMEK的有效性。 关键词: 模式识别 / 熵理论 / 核密度估计 / 支持向量机" @default.
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- W2317395801 date "2011-09-30" @default.
- W2317395801 modified "2023-09-26" @default.
- W2317395801 title "A Maximum Margin Learning Machine Based on Entropy Concept and Kernel Density Estimation" @default.
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- W2317395801 doi "https://doi.org/10.3724/sp.j.1146.2010.01434" @default.
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