Machine Learning Research: Four Current Directions (1997)
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BibTeX
@MISC{Dietterich97machinelearning,
author = {Thomas G. Dietterich},
title = {Machine Learning Research: Four Current Directions},
year = {1997}
}
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Abstract
Machine Learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models. 1 Introduction The last five years have seen an explosion in machine learning research. This explosion has many causes. First, separate research communities in symbolic machine learning, computational learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine learning techniques are being applied to new kinds of problems including knowledge discovery in databases, language processing, robot control, and combinatorial optimization as well as in more traditional problems such as speech recognition, face recognitio...
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