- (Birattari et al., 1999) ⇒ Mauro Birattari, Gianluca Bontempi, and Hugues Bersini. (1999). “Lazy Learning Meets the Recursive Least Squares Algorithm.” In: Proceedings of the 1998 conference on Advances in Neural Information Processing Systems II.
Subject Headings: Lazy Model-based Learning Algorithm.
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a data-driven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to identify and validate local models, the recursive least squares algorithm is introduced in the context of local approximation and lazy learning. Furthermore, beside the winner-takes-all strategy for model selection, a local combination of the most promising models is explored. The method proposed is tested on six different datasets and compared with a state-of-the-art approach.
|1999 LazyLearningMeetstheRecursiveLe||Mauro Birattari|
|Lazy Learning Meets the Recursive Least Squares Algorithm||1999|