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Abstract:
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.
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