G.L. Bontempi, M. Birattari, H. Bersini, Lazy learning for local modeling and control design, Int. J. Contr. 72 (7/8) (1998) 643--658. @ NUS Home/Search Document Details and Download
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The Lazy Learning Toolbox - Bontempi (1999) Self-citation (Birattari) (Correct)
....functions, written in C language, that implement the lazy learning methods for regression developed at IRIDIA, Universit e Libre de Bruxelles. The software is part of a larger IRIDIA project, whose goal is the implementation of a set of local modeling approaches for data analysis and regression (Bontempi et al. 1999). The aim of this manual is to provide the user with a description of the functions composing the toolbox. Nevertheless, we try also to present in a formal way the algorithms we developed, and the underlying theory. 2 Technical Report TR IRIDIA 99 7. Iridia, Universit e Libre de Bruxelles. The ....
....a database of examples. Legal issues and conditions By using the toolbox the user agrees to all of the following: ffl If any work where this toolbox has been used is going to publish, please remember that the software was obtained free of charge and please include a reference to: Birattari M. Bontempi G. Bersini H. 1999. Lazy learning meets the recursive least squares algorithm , in Advances in Neural Information Processing Systems 11, M.S. Kearns, S.A. Solla, and D.A. Cohn, Eds. MIT Press, Cambridge, MA. and the url of the toolbox home page: http: iridia.ulb.ac.be lazy . If the work is in the control ....
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Bontempi G. , Birattari M. & Bersini H. 1999. Lazy learning for local modeling and control design. International Journal of Control. vol. 72, no. 7/8, pp.
Lazy Learning Meets the Recursive Least Squares Algorithm - Birattari, Bontempi, Bersini (1999) (3 citations) Self-citation (Birattari Bersini) (Correct)
....statistic of their cross validation errors. The main reason to favor a query by query bandwidth selection is that it allows better adaptation to the local characteristics of the problem at hand. Moreover, this approach is able to handle directly the case in which the database is updated on line (Bontempi et al. 1997). On the other hand, a globally optimized bandwidth approach would, in principle, require the global optimization to be repeated each time the distribution of the examples changes. The major contribution of the paper consists in the adoption of the recursive least squares algorithm in the context ....
Bontempi G. , Birattari M. & Bersini H. 1997. Lazy learning for local modeling and control design. International Journal of Control. Accepted for publication.
Vector Quantization: A Weighted Version For.. - Lendasse, Francois, .. (2005) (Correct)
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G.L. Bontempi, M. Birattari, H. Bersini, Lazy learning for local modeling and control design, Int. J. Contr. 72 (7/8) (1998) 643--658.
Nonlinear Time Series Prediction by Weighted Vector.. - Lendasse, Francois.. (2003) (Correct)
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Bontempi, G.L., Birattari, M., Bersini, H.: Lazy Learning for Local Modeling and Control Design, International Journal of Control, 72(7/8), (1998) 643--658.
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