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Dyadic Decision Trees This thesis introduces a new family of classifiers called dyadic decision trees (DDTs) and develops their theoretical properties within the framework of statistical learning theory. First, we show that DDTs achieve optimal rates of convergence for a broad range of classification problems and are adaptive in three important respects:(More)
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