论文标题
前馈神经网络的线性判别初始化
Linear discriminant initialization for feed-forward neural networks
论文作者
论文摘要
在馈送前馈神经网络的基本几何形状的告知下，我们使用最能区分单个类别的线性判别因子初始化了神经网络的第一层的权重。以这种方式初始化的网络采用更少的培训步骤来达到相同的培训水平，并且渐近地在培训数据上具有更高的准确性。
Informed by the basic geometry underlying feed forward neural networks, we initialize the weights of the first layer of a neural network using the linear discriminants which best distinguish individual classes. Networks initialized in this way take fewer training steps to reach the same level of training, and asymptotically have higher accuracy on training data.
