Abstract
Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement.
| Translated title of the contribution | Harmonia loosely praestabilita: discovering adequate inductive strategies |
|---|---|
| Original language | English |
| Title of host publication | Proceedings of the 22nd Annual Meeting of the Cognitive Science Society |
| Publisher | Cognitive Science Society |
| Pages | 609 - 614 |
| Number of pages | 5 |
| Publication status | Published - 2000 |
Bibliographical note
Other page information: 609-614Conference Proceedings/Title of Journal: Proceedings of the 22nd Annual Meeting of the Cognitive Science Society
Other identifier: 1000501