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In this work, the localized generalization error model (L-GEM) for multilayer perceptron neural network (MLPNN) is derived. The L-GEM is inspired by the fact that a classifier should not be required to recognize unseen samples that are very different from the training samples. Therefore, evaluating a classifier by very different unseen samples may be counter-productive. In the L-GEM, the ldquolocalrdquo...
The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving...
We had developed the localized generalization error model for supervised learning with minimization of mean square error. In this work, we extend the error model to single layer perceptron neural network (SLPNN). For a trained SLPNN and a given training dataset, the proposed error model bounds above the error for unseen samples which are similar to the training samples. This pilot study is the important...
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...
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