We have now a large number of learning algorithms available. What works well where? In order to find correlations between areas of expertise of learning algorithms and learning tasks, we can resort to \em meta-learning. Several meta-learning scenarios have been proposed. In one scenario, we are searching for the best learning algorithm for a problem. The decision can be made using different strategies. In any approach to meta-learning, it is crucial to choose relevant features to describe a task. Different strategies of task description have been proposed: some strategies based on statistical features of the dataset, some based on information-theoretic properties, others based on a learning algorithm's representation of the task. In this work we present a novel approach to task description, called landmarking.
|Translated title of the contribution||What works well tells us what works better|
|Title of host publication||Proceedings of ICML'2000 workshop on What Works Well Where|
|Publication status||Published - 2000|
Bibliographical noteOther page information: 1-8
Conference Proceedings/Title of Journal: Proceedings of ICML'2000 workshop on What Works Well Where
Other identifier: 1000469