Landmarking is a novel approach to describing tasks in meta-learning. Previous approaches to meta-learning mostly considered only statistics-inspired measures of the data as a source for the definition of meta-attributes. Contrary to such approaches, landmarking tries to determine the location of a specific learning problem in the space of all learning problems by directly measuring the performance of some simple and efficient learning algorithms themselves. In the experiments reported we show how such a use of landmark values can help to distinguish between areas of the learning space favouring different learners. Experiments, both with artificial and real-world databases, show that landmarking selects, with moderate but reasonable level of success, the best performing of a set of learning algorithms.
|Translated title of the contribution||Meta-learning by landmarking various learning algorithms|
|Journal||Proceedings of the Seventeenth International Conference on Machine Learning (ICML'2000)|
|Publication status||Published - 2000|
Bibliographical noteISBN: 1558607072
Publisher: Morgan Kaufmann
Name and Venue of Conference: Proceedings of the Seventeenth International Conference on Machine Learning, ICML'2000
Other identifier: 1000467