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中文摘要：
基于Domingos的期望预测误差分解框架,在3个数据集上,对MCLP、LDA和C5.0这3种算法的偏差-方差结构特点进行了比较分析.实验结果表明,一般来说,C5.0呈现低偏差-高方差的特点,LDA与之相反,而MCLP则介于两者之间,比较接近LDA.当训练集样本量较小时,MCLP的偏差和方差都相对较高,而随着训练集的增大,MCLP的偏差和方差明显减小,甚至低于其他两者.
英文摘要：
Based on Domingos＇ bias-variance decomposition framework, on three different data sets, we compared the bias-variance structure of the three classification methods： MCLP, LDA and C5.0. The experimental results showed that, generally speaking, C5.0 has low bias and high variance, LDA has high bias and low variance, and MCLP is in between them but near LDA. When the training set is small, bias and variance of MCLP is comparatively high. However, with the increasing of training set, bias and variance of MCLP obviously decrease and even are lower than those of C5.0 and LDA. This study established the basis for constructing the ensemble suited to MCLP.
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