This work presents a technique that integrates the backpropagation learning method with a method to calculate the initial weights in order to train the Multilayer Perceptron Model. The method to calculate the initial weights of the MLP is based on the quality of similarity measure proposed on the framework of the extended Rough Set Theory. Experimental results show that the proposed initialization method performs better than other methods used to calculate the weight of the features, so it is an interesting alternative to the conventional random initialization. © 2011 Springer-Verlag.
CITATION STYLE
Filiberto Cabrera, Y., Bello Pérez, R., Mota, Y. C., & Jimenez, G. R. (2011). Improving the MLP learning by using a method to calculate the initial weights of the network based on the quality of similarity measure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7095 LNAI, pp. 351–362). https://doi.org/10.1007/978-3-642-25330-0_31
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