The lazy learning toolbox, for use with matlab (1999) [4 citations — 1 self]
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by Mauro Birattari, Gianluca Bontempi
ftp://iridia.ulb.ac.be/pub/lazy/papers/IridiaTr1999-07.ps.gz
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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. This toolbox implements 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 adopted. Furthermore, beside the winner-takes-all strategy for model selection, the toolbox implements also a local combination, performed on a query-by-query basis, of the most promising models. This manual describes the functions included in the toolbox as well as
