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Adaptation of the Regularization Parameters in the Nm-Delta Networks
International Journal of Applied Mathematics and Computer Science, Tome 10 (2000) no. 4, pp. 779-790 Cet article a éte moissonné depuis la source Library of Science

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The paper describes an application of regularization techniques to an automatic choice of parameters driving the learning process in the NM-Delta neural network architecture. The heterogeneous learning algorithm is identified as very similar to the Levenberg-Marquardt method but with a considerably smaller computational cost and different justification of parameter selection. The performance of the modified algorithm proves to be comparable with that of the Levenberg-Marquardt.
Keywords: NM-Delta, M-Delta, regularization, Levenberg-Marquardt, quasi-Newton
Mots-clés : regularyzacja, Levenberg-Marquardt 
@article{IJAMCS_2000_10_4_a6,
     author = {Go{\l}\k{a}bek, P. and Kosi\'nski, W.},
     title = {Adaptation of the {Regularization} {Parameters} in the {Nm-Delta} {Networks}},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {779--790},
     year = {2000},
     volume = {10},
     number = {4},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2000_10_4_a6/}
}
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Gołąbek, P.; Kosiński, W. Adaptation of the Regularization Parameters in the Nm-Delta Networks. International Journal of Applied Mathematics and Computer Science, Tome 10 (2000) no. 4, pp. 779-790. http://geodesic.mathdoc.fr/item/IJAMCS_2000_10_4_a6/