Abstract
We treat Bayesian neural networks adapted to changes in the ratio of prior probabilities of the categries. If an ordinary Bayesian neural network is equipped with m–1 additional input units, it can learn simultaneously m distinct discriminant functions which correspond to the m different ratios of the prior probabilities.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Ito, Y., Srinivasan, C., Izumi, H. (2005). Bayesian Learning of Neural Networks Adapted to Changes of Prior Probabilities. 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_40
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DOI: https://doi.org/10.1007/11550907_40
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