Abstract
Visualization of MLP error surfaces helps to understand the influence of network structure and training data on neural learning dynamics. PCA is used to determine two orthogonal directions that capture almost all variance in the weight space. 3-dimensional plots show many aspects of the original error surfaces.
Cite
CITATION STYLE
APA
Kordos, M., & Duch, W. (2004). On some factors influencing MLP error surface. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3070, pp. 217–222). Springer Verlag. https://doi.org/10.1007/978-3-540-24844-6_28
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