Abstract
In Non Linear Discriminant Analysis (NLDA) an MLP like architecture is used to minimize a Fisher’s discriminant analysis criterion function. In this work we study the architecture selection problem for NLDA networks. We shall derive asymptotic distribution results for NLDA weights, from which Wald like tests can be derived. We also discuss how to use them to make decisions on unit relevance based on the acceptance or rejection of a certain null hypothesis.
Cite
CITATION STYLE
Dorronsoro, J. R., González, A. M., & Cruz, C. S. (2001). Architecture selection in NLDA networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 27–32). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_5
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