Abstract
Task description is crucial not only to every meta-learning enterprise but also to related endeavours like transfer of learning. This paper evaluates the performance of a newly introduced method of task description, landmarking, in a supervised meta-learning scenario. The method relies on correlations between simple and more sophisticated learning algorithms to select the best learner for a task. The results compare favourably with an information-based method and suggest that landmarking holds promise.
| Translated title of the contribution | Casa Batlo is in Passeig de Gracia or landmarking the expertise space |
|---|---|
| Original language | English |
| Title of host publication | Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination |
| Publisher | ECML'2000 |
| Pages | 29 - 47 |
| Number of pages | 18 |
| Publication status | Published - 2000 |
Bibliographical note
Other page information: 29-47Conference Proceedings/Title of Journal: Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination
Other identifier: 1000470