Geodesic
Second-order learning methods for a multilayer perceptron
Matematičeskoe modelirovanie, Tome 10 (1998) no. 3, pp. 117-124 Cet article a éte moissonné depuis la source Math-Net.Ru

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First-and second-order learning methods for feed-forward multilayer networks are studied. Newtontype and quasi-Newton algorithms are considered and compared with commonly used backpropagation algorithm. It is shown that, although second-order algorithms reguire enhanced computer facilities, they provide better convergence and simplicity in usage. 
@article{MM_1998_10_3_a8,
     author = {V. V. Ivanov and B. Purevdorj and I. V. Puzynin},
     title = {Second-order learning methods for a~multilayer perceptron},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {117--124},
     year = {1998},
     volume = {10},
     number = {3},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_1998_10_3_a8/}
}
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V. V. Ivanov; B. Purevdorj; I. V. Puzynin. Second-order learning methods for a multilayer perceptron. Matematičeskoe modelirovanie, Tome 10 (1998) no. 3, pp. 117-124. http://geodesic.mathdoc.fr/item/MM_1998_10_3_a8/