Learning and Generalization in Cascade Network Architectures

Littmann E, Ritter H (1996)
Neural Computation 8(7): 1521-1539.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Littmann, Enno; Ritter, HelgeUniBi
Abstract / Bemerkung
Incrementally constructed cascade architectures are a promising alternative to networks of predefined size. This paper compares the direct cascade architecture (DCA) proposed in Littmann and Ritter (1992) to the cascade-correlation approach of Fahlman and Lebiere (1990) and to related approaches and discusses the properties on the basis of various benchmark results. One important virtue of DCA is that it allows the cascading of entire subnetworks, even if these admit no error-backpropagation. Exploiting this flexibility and using LLM networks as cascaded elements, we show that the performance of the resulting network cascades can be greatly enhanced compared to the performance of a single network. Our results for the Mackey-Glass time series prediction task indicate that such deeply cascaded network architectures achieve good generalization even on small data sets, when shallow, broad architectures of comparable size suffer from overfitting. We conclude that the DCA approach offers a powerful and flexible alternative to existing schemes such as, e.g., the mixtures of experts approach, for the construction of modular systems from a wide range of subnetwork types.
Erscheinungsjahr
1996
Zeitschriftentitel
Neural Computation
Band
8
Ausgabe
7
Seite(n)
1521-1539
eISSN
1530-888X
Page URI
https://pub.uni-bielefeld.de/record/2715186

Zitieren

Littmann E, Ritter H. Learning and Generalization in Cascade Network Architectures. Neural Computation. 1996;8(7):1521-1539.
Littmann, E., & Ritter, H. (1996). Learning and Generalization in Cascade Network Architectures. Neural Computation, 8(7), 1521-1539. doi:10.1162/neco.1996.8.7.1521
Littmann, E., and Ritter, H. (1996). Learning and Generalization in Cascade Network Architectures. Neural Computation 8, 1521-1539.
Littmann, E., & Ritter, H., 1996. Learning and Generalization in Cascade Network Architectures. Neural Computation, 8(7), p 1521-1539.
E. Littmann and H. Ritter, “Learning and Generalization in Cascade Network Architectures”, Neural Computation, vol. 8, 1996, pp. 1521-1539.
Littmann, E., Ritter, H.: Learning and Generalization in Cascade Network Architectures. Neural Computation. 8, 1521-1539 (1996).
Littmann, Enno, and Ritter, Helge. “Learning and Generalization in Cascade Network Architectures”. Neural Computation 8.7 (1996): 1521-1539.

2 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Neural network for graphs: a contextual constructive approach.
Micheli A., IEEE Trans Neural Netw 20(3), 2009
PMID: 19193509
Adaptive color segmentation-a comparison of neural and statistical methods.
Littmann E, Ritter H., IEEE Trans Neural Netw 8(1), 1997
PMID: 18255622

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