A universal model based on minimax average divergence

Given a set of training samples, the commonly used approach to determine a universal model is accomplished by averaging the statistics over all training samples. It is suggested to use average divergence as a measurement for the effectiveness of a universal model and a minimax universal model that minimizes the maximum average divergence among all training samples is proposed. Efficient searching algorithms are developed and experimental results are presented.<
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