Systems that can learn from examples: Replica calculation of uniform convergence bounds for perceptrons.
Systems that can learn from examples: Replica calculation of uniform convergence bounds for perceptrons.
复制标题
可以从示例中学习的系统：感知器均匀收敛边界的复制计算。
DOI：
10.1103/physrevlett.71.1772
复制
发表时间：
1993
影响因子：
8.6
通讯作者：
Van den Broeck C
中科院分区：
文献类型：
--
作者：
Engel;Van den Broeck C
神经网络基于实例推理规则的泛化能力可以用随着训练集规模的增加学习误差收敛到泛化误差来表征。使用副本技术，我们计算训练和泛化误差之间的最大差异的所有感知器训练的教师感知器和感知器的训练误差等于零的最大泛化误差的合奏。结果与Vapnik-Chervonenkis定理提供的严格界进行了比较
The generalization abilities of neural networks for inferring a rule on the basis of examples can be characterized by the convergence of the learning error to the generalization error with increasing size of the training set. Using the replica technique, we calculate the maximum difference between training and generalization error for the ensemble of all perceptrons trained by a teacher perceptron and the maximal generalization error for the perceptrons that have a training error equal to zero. The results are compared with the rigorous bounds provided by the Vapnik-Chervonenkis theorem