TR/IRIDIA/1999-006info:eu-repo/semantics/publishe

Birattari, Mauro

Bontempi, Gianluca

DI-fusion

Lazy Learning Vs. Speedy Gonzales: A fast algorithm for recursive identification and recursive validation of local constant models

In this paper we propose a recursive method for identifying and crossvalidating 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 definition of a metric and local combination of estimators, and we focus our attention on the derivation of an efficient 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. 1 Introduction  In this paper we deal with the classical problem of supervised learning. A set of examples f(x i ; y i )g  n i=1 is given, where 8i x i 2 !  m  and y i 2 !. From this dataset, we wish to learn the real mapping f : !  m  7! ! which is implicitly supposed to have somewhat informed the generation of the examples. In particular we want to be able to generalize, i.e. to predict ..

Mauro Birattari

Gianluca Bontempi

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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6427

Lazy Learning Vs. Speedy Gonzales: A fast algorithm for recursive identification and recursive validation of local constant models

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