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Abstract : |
The learning curve of Bayes optimal classification algorithm when learning a perceptron from noisy random training examples is calculated exactly in the limit of large training sample size and large instance space dimension using methods of statistical mechanics. It is shown that under certain assumptions, in this "thermodynamic " limit, the probability of misclassification of Bayes optimal algorithm is less than that of a canonical stochastic learning algorithm, by a factor approaching, |
