Description Details Author(s) References
This package implements a regularized version of ELM. Regularization is performed using a priori spacial information expressed by an affinity matrix. The use of this type of a priori information is similar to perform Tikhonov regularization. Furthermore, if a parameter free affinity matrix is used, like the cosine similarity matrix, regularization is performed without any need for parameter tunning. This version is applicable for binary classification problems.
Package: | amrElm |
Type: | Package |
Version: | 1.0 |
Date: | 2014-12-03 |
License: | MIT |
To create the model, the function to use is amrElmTrain
. To predict values (or to test the
model), the function to use is amrElmTest
.
Leonardo José Silvestre
Maintainer: Leonardo José Silvestre <leonardo.silvestre@ufes.br>
SILVESTRE, L. J. ; LEMOS, A. P. ; BRAGA, J. P. ; Braga, A. P. . Dataset Structure as Prior
Information for Parameter-Free Regularization of Extreme Learning Machines In: Neurocomputing (accepted for publication).
SILVESTRE, L. J. ; LEMOS, A. P. ; BRAGA, J. P. ; Braga, A. P. . Parameter-free regularization in Extreme Learning Machines with affinity matrices. In: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014, 23-25 April 2014, Bruges, Belgium. Louvain-la-Nueve, Belgique, 2014. p. 595-600.
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