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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 ... (Update)
Context of citations to this paper: More
...the local polynomial approximator. implementations of memory based methods can be found in Moore et al. 15] and in Birattari et al. [16]. As far as the problem of retrieving relevant data is concerned, further references can be found in the comprehensive tutorial on local...
.... remaining problem of finding the optimal scaling factor is equivalent to the problem of bandwidth selection in univariate smoothing [FG96, BBB99]. For example, the bandwidth is frequently chosen as a function of the distance between x 0 and its kth nearest neighbor in...
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BibTeX entry: (Update)
M. Birattari, G. Bontempi, and H. Bersini. Lazy learning meets the recursive least-squares algorithm. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, Cambridge, 1999. MIT Press. http://citeseer.comp.nus.edu.sg/123465.html More
@misc{ birattari99lazy,
author = "M. Birattari and G. Bontempi and H. Bersini",
title = "Lazy learning meets the recursive least-squares algorithm",
text = "M. Birattari, G. Bontempi, and H. Bersini. Lazy learning meets the recursive
least-squares algorithm. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors,
Advances in Neural Information Processing Systems 11, Cambridge, 1999. MIT
Press.",
year = "1999",
url = "citeseer.comp.nus.edu.sg/123465.html" }
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