Abstract
We consider the practical advantage of the Bayesian approach over maximum a posteriori methods in its ability to smoothen the landscape of generalization performance measures in the space of hyperparameters, which is vitally important for determining the optimal hyperparameters. The variational method is used to approximate the intractable distribution. Using the leave-one-out error of support vector regression as an example, we demonstrate a further advantage of this method in the analytical estimation of the leave-one-out error, without doing the cross-validation. Comparing our theory with the simulations on both artificial (the “sinc” function) and benchmark (the Boston Housing) data sets, we get a good agreement.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Gao, Z., Wong, K.Y.M. (2005). Smooth Performance Landscapes of the Variational Bayesian Approach. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_38
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DOI: https://doi.org/10.1007/11550907_38
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