Abstract

Machine learning algorithms search a space of possible hypotheses and estimate the error of each hypotheses using a sample. Most often, the goal of classification tasks is to find a hypothesis with a low true (or generalization) misclassification probability (or error rate); however, only the sample (or empirical) error rate can actually be measured and minimized. The true error rate of the returned hypothesis is unknown but can, for instance, be estimated using cross validation, and very general worst-case bounds can be given. This doctoral dissertation addresses a compound of questions on error assessment and the intimately related selection of a "good" hypothesis language, or learning algorithm, for a given problem. In the first

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