This paper presents two algorithms to aid the supervised learning of feedforward neural networks. Specifically, an initialization and a learning algorithm are presented. The proposed methods are based on the independent optimization of a subnetwork using linear least squares. An advantage of these methods is that the dimensionality of the effective search space for the non-linear algorithm is reduced, and therefore it decreases the number of training epochs which are required to find a good solution. The performance of the proposed methods is illustrated by simulated examples. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Fontenla-Romero, O., Erdogmus, D., Principe, J. C., Alonso-Betanzos, A., & Castillo, E. (2003). Linear least-squares based methods for neural networks learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 84–91. https://doi.org/10.1007/3-540-44989-2_11
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