论文标题
有监督的关于前馈神经网络的HEBBIAN学习方法
A Supervised Modified Hebbian Learning Method On Feed-forward Neural Networks
论文作者
论文摘要
在本文中，我们提出了一种基于Hebbian学习算法的新的监督学习算法，以尝试替代背部传播以及梯度下降，以替代生物学上更合理的方法。该算法的最佳性能是在使用MNIST手写数字数据集的馈送神经网络上运行的，在测试数据集上的精度为70.4％，验证数据集的精度为71.48％。
In this paper, we present a new supervised learning algorithm that is based on the Hebbian learning algorithm in an attempt to offer a substitute for back propagation along with the gradient descent for a more biologically plausible method. The best performance for the algorithm was achieved when it was run on a feed-forward neural network with the MNIST handwritten digits data set reaching an accuracy of 70.4% on the test data set and 71.48% on the validation data set.
