MDL Learning of Probabilistic NeuralNetworks for Discrete Problem
نویسنده
چکیده
| Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to nd the solution, possibly modifying retrieved cases to adapt to the required input speciications. In discrete domains CBR reasoning can be based on a rigorous Bayesian probability propagation algorithm. Such a Bayesian CBR system can be implemented as a probabilistic feedforward neural network with one of the layers representing the cases. In this paper we introduce a Minimum Description Length (MDL) based learning algorithm to obtain the proper network structure with the associated conditional probabilities. This algorithm together with the resulting neural network implementation provide a massively parallel architecture for solving the eeciency bottleneck in case-based reasoning.
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