Showing papers by "Thomas G. Dietterich published in 1990"

A comparative study of ID3 and backpropagation for English text-to-speech mapping

Abstract: The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping.

Editorial Exploratory research in machine learning

Abstract: Exploratory research contributes to the continued vitality of every discipline. The aim of exploratory research is to identify new tasks--tasks that cannot be solved by existing methods. Once a new task has been found, exploratory research seeks to develop a precise definition of the task and to understand the factors that make the task different from previously-solved tasks. Until recently, most research in machine learning was primarily exploratory. However, during the past decade, some areas of the field--particularly inductive learning--have matured to the point that careful, quantitative experiments are now possible and proved theoretical results have been obtained. Although these trends are extremely healthy and long overdue, there is a danger that the increased attention to these products of mature research may discourage researchers from undertaking and publishing research of a more exploratory nature. The goal of this editorial is to emphasize the importance of exploratory research and to encourage the publication of high quality exploratory results in Machine Learning.