论文标题
广义感知器学习
Generalised Perceptron Learning
论文作者
论文摘要
我们将Rosenblatt的传统感知性学习算法的概括介绍给了近端激活功能类别，并证明了如何将这种概括解释为应用于新型能量功能的增量梯度方法。这种新型的能量函数基于广义的布雷格曼距离，为此，相对于权重和偏见的梯度不需要分化激活函数。解释为一种能量最小化算法为许多新算法铺平了道路，我们探索了迭代软势阈值的新型变体，以学习稀疏感知器的学习。
We present a generalisation of Rosenblatt's traditional perceptron learning algorithm to the class of proximal activation functions and demonstrate how this generalisation can be interpreted as an incremental gradient method applied to a novel energy function. This novel energy function is based on a generalised Bregman distance, for which the gradient with respect to the weights and biases does not require the differentiation of the activation function. The interpretation as an energy minimisation algorithm paves the way for many new algorithms, of which we explore a novel variant of the iterative soft-thresholding algorithm for the learning of sparse perceptrons.
