Andrew Borden

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Our problem is to build a maximally efficient Bayesian classifier when each parameter has a different cost and provides a different amount of information toward the solution. This is an extremely computationally expensive problem. We accept a sub-optimal, although demonstrably good, solution based on Shannon's definition of Information and Uncertainty. Our(More)
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