南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (2): 340.
屈伟洋,俞 扬*
Qu Weiyang,Yu Yang*
摘要: 传统神经网络训练方法通过计算输出Y和目标T之间误差,并将该误差反向传递,用以修改节点权重,并不断重复该过程直至达到预期结果.该方法在模型训练时存在收敛较慢、容易过度拟合的问题.多样性正则项(diversity regularization)最近显示出有简化模型、提高泛化能力的作用,对带有多样性正则项的神经网络训练方法进行探索,在计算目标函数时加入权重多样性的考虑,从而使得网络的内部结构减少重复.与传统神经网络训练方法——反向传播算法(back?propagation algorithm,BP)和目标差传播方法(difference target propagation,DTP)的结合与对比实验表明,带多样性正则项的训练方法具有更快的收敛速度和较低的错误率.
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