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bp-extensions
- 利用belief propagation方法实现的模型优化和参数的估计-Ways to achieve the use of belief propagation model optimization and parameter estimation
Anity-Propagation
- 关于最新ap算法的开创性重要文章,应用数据分类-Affi nity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplar-based clustering. We present a deriva- tion of AP that is much simpler than the original one and is based on a qu
Untitled2
- BP神经网络基本原理概述:这种网络模型利用误差反向传播训练算法模型,能够很好地解决多层网络中隐含层神经元连接权值系数的学习问题,它的特点是信号前向传播、误差反向传播,简称BP(Back Propagation)神经网络。BP学习算法的基本原理是梯度最快下降法,即通过调整权值使网络总误差最小,在信号前向传播阶段,输入信号经输入层处理再经隐含层处理最后传向输出层处理;在误差反向传播阶段,将输出层输出的信号值与期望输出信号值比较得到误差,若误差较大则把误差信号传回隐含层直至输入层,在各层神经元中使用