Gödel Prize - 2003 |
"A Decision Theoretic Generalization of On-Line Learning and an
Application to Boosting"
Journal of Computer and System
Sciences 55 (1997), pp. 119-139.
This paper introduced AdaBoost, an adaptive algorithm to improve the accuracy of hypotheses in machine learning. The algorithm demonstrated novel possibilities in analysing data and is a permanent contribution to science even beyond computer science.
Because of a combination of features, including its elegance, the simplicity of its implementation, its wide applicability, and its striking success in reducing errors in benchmark applications even while its theoretical assumptions are not known to hold, the algorithm set off an explosion of research in the fields of statistics, artificial intelligence, experimental machine learning, and data mining. The algorithm is now widely used in practice.
The paper highlights the fact that theoretical computer science continues to be a fount of powerful and entirely novel ideas with significant and direct impact even in areas, such as data analysis, that have been studies extensively by other communities.