AI Article Synopsis
- The Kullback-Leibler (KL) distance framework reveals that a specific Gaussian probability function in feedforward neural networks simplifies to the first-order Tikhonov regularizer.
- The smooth parameter in kernel density estimation acts as the regularization parameter, leading to an estimation formula derived from approximations of training data sets.
- Comparisons with prior research highlight both similarities and differences, with experiments demonstrating the effectiveness of the estimation formula in scenarios with sparse and small training samples.
Article Abstract
Under the framework of the Kullback-Leibler (KL) distance, we show that a particular case of Gaussian probability function for feedforward neural networks (NNs) reduces into the first-order Tikhonov regularizer. The smooth parameter in kernel density estimation plays the role of regularization parameter. Under some approximations, an estimation formula is derived for estimating regularization parameters based on training data sets. The similarity and difference of the obtained results are compared with other work. Experimental results show that the estimation formula works well in sparse and small training sample cases.
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| http://dx.doi.org/10.1109/TSMCB.2003.808176 | DOI Listing |
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