Statistical Sensitivity Measure of Single Layer Perceptron Neural Networks to Input Perturbation
Xiaoqin Zeng, Wing W. Y. Ng, Daniel So Yeung · 2006
In this work, we study the statistical output sensitivity measure of a trained single layer preceptron neural network to input perturbation. This quantitative measure computes the expectation of absolute output deviations due to input perturbation with respect to all possible inputs. This is an important first step to the study of the statistical output sensitivity measure of multilayer perceptron neural networks. The major contribution of this work is the relaxation of the restriction of the input having uniform distributions in our early studies. Therefore, the novel sensitivity measure is applicable to real world applications such as machine learning problems. Furthermore, experimental results show that the new sensitivity measure is suitable to the networks with large input dimension.