The cascade-correlation learning architecture
Advances in neural information processing systems 2
Neural networks and the bias/variance dilemma
Neural Computation
Bias/variance analyses of mixtures-of-experts architectures
Neural Computation
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Learning Translation Invariant Recognition in Massively Parallel Networks
Proceedings of the Parallel Architectures and Languages Europe, Volume I: Parallel Architectures PARLE
Classification of healthy and abnormal swallows based on accelerometry and nasal airflow signals
Artificial Intelligence in Medicine
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Recently, a combined approach of bagging (bootstrap aggregating) and noise addition was proposed and shown to result in a significantly improved generalization performance. But, the level of noise introduced, a crucial factor, was determined by trial and error. The procedure is not only ad hoc but also time consuming since bagging involves training a committee of networks. Here we propose a principled procedure of computing the level of noise, which is also computationally less expensive. The idea comes from kernel density estimation (KDE), a non-parametric probability density estimation method where appropriate kernel functions such as Gaussian are imposed on data. The kernel bandwidth selector is a numerical method for finding the width of a kernel function (called bandwidth). The computed bandwidth can be used as the variance of added noise. The proposed approach makes the trial and error procedure unnecessary, and thus provides a much faster way of finding an appropriate level of noise. In addition, experimental results show that the proposed approach results in an improved performance over bagging, particularly for noisy data.