存在数据抖动的多层感知器中基于查询的学习

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213500

Seho Oh, R. Marks, M. El-Sharkawi

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引用次数: 11

摘要

随机扰动的特征数据被称为抖动。抖动数据在分类(或回归)空间中具有卷积平滑效果。抖动的参数化知识可以用来扰动神经网络的训练代价函数，从而提高训练效率。当在基于查询的学习过程中使用附加代价函数时，改进更为显著。

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Query based learning in a multilayered perceptron in the presence of data jitter

Stochastically perturbed feature data is said to be jittered. Jittered data has a convolutional smoothing effect in the classification (or regression) space. Parametric knowledge of the jitter can be used to perturb the training cost function of the neural network so that more efficient training can be performed. The improvement is more striking when the addended cost function is used in a query based learning procedure.<>

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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems

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