2004
Authors
Giraud Carrier, C; Vilalta, R; Brazdil, P;
Publication
MACHINE LEARNING
Abstract
2004
Authors
Leite, R; Brazdil, P;
Publication
MACHINE LEARNING: ECML 2004, PROCEEDINGS
Abstract
This paper describes a method that can be seen as an improvement of, the standard progressive sampling. The standard method uses samples of data of increasing size until accuracy of the learned concept cannot be further improved. The issue we have addressed here is how to avoid using some of the samples in this progression. The paper presents a method for predicting the stopping point using a meta-learning approach. The method requires just four iterations of the progressive sampling. The information gathered is used to identify the nearest learning curves, for which the sampling procedure was carried out fully. This in turn permits to generate the prediction regards the stopping point. Experimental evaluation shows that the method can lead to significant savings of time without significant losses of accuracy.
2004
Authors
Cordeiro, J; Brazdil, P;
Publication
Pattern Recognition in Information Systems, Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems, PRIS 2004, In conjunction with ICEIS 2004, Porto, Portugal, April 2004
Abstract
2004
Authors
Leite, R; Brazdil, P;
Publication
Pattern Recognition in Information Systems, Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems, PRIS 2004, In conjunction with ICEIS 2004, Porto, Portugal, April 2004
Abstract
2004
Authors
Camacho, R; King, RD; Srinivasan, A;
Publication
ILP
Abstract
2004
Authors
Camacho, R; King, R; Srinivasan, A;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
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