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.
With partial support from Spain’s CICyT, grant TIC 98-247.
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© 2001 Springer-Verlag Berlin Heidelberg
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Dorronsoro, J.R., González, A.M., Cruz, C.S. (2001). Architecture Selection in NLDA Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_5
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DOI: https://doi.org/10.1007/3-540-44668-0_5
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