Abstract not found

This paper discusses the intimate relationships between the supervised learning frameworks mentioned in the title. In particular, it shows how all those frameworks can be viewed as particular instances of a single overarching formalism. In doing this many commonly misunderstood aspects of those frameworks are explored. In addition the strengths and weaknesses of those frameworks are compared, and some novel frameworks are suggested (resulting, for example, in a "correction" to the familiar bias-plus-variance formula).

In order to select a good hypothesis language (or model) from a collection of possible models, one has to assess the generalization performance of the hypothesis which is returned by a learner that is bound to use some particular model. This paper deals with a new and very efficient way of assessing this generalization performance. We present a new analysis which characterizes the expected generalization error of the hypothesis with least training error in terms of the distribution of error rates of the hypotheses in the model. This distribution can be estimated very efficiently from the data which immediately leads to an efficient model selection algorithm. The analysis predicts learning curves with a very high precision and thus contributes to a better understanding of why and when over-fitting occurs. We present empirical studies (controlled experiments on Boolean decision trees and a large-scale text categorization problem) which show that the model selection algorithm leads to err...

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.