Lazy Learning vs. Speedy Gonzales: a Fast Algorithm for Recursive Identiication and Recursive Validation of Local Constant Models

In this paper we propose a recursive method for identifying and cross-validating local constant models. The algorithm we derive here is intended to be a part of a more general lazy learning method already presented by the authors (Birattari et al., 1999). We take for granted aspects related to the search of the nearest-neighbors, the deenition of a metric and local combination of estimators, and we focus our attention on the derivation of an eecient way to obtain and assess a sequence of local constant models centered on a given query point, and each including a growing number of nearest-neighbors.