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Title: On the robustness of learning in the multi-layer perceptron

Creator:

Williams, Paul ; Duller, Andrew W.G.

Date:

1995

Resource Type:

artykuł

Contributor:

Korbicz, Józef - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, volume 5 (1995)

Abstract:

The back-propagation algorithm, for training multi-layer perceptrons tends to be slow to converge to a final solution and many methods have been proposed for improving this. One technique takes advantage of an alternative training error criterion, however, we show that this reduces the robustness of the learning in the presence of outliers in the input data. Two examples are used to show the characteristics of the learning methods, one a test problem and the other from a "real-world" problem.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:57710

Pages:

743-749

Source:

AMCS, volume 5, number 4 (1995) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

sterowanie sterowanie-teoria sztuczna inteligencja matematyka stosowana informatyka

Object collections:

Last modified:

Sep 2, 2021

In our library since:

Sep 21, 2020

Number of object content hits:

68

All available object's versions:

https://zbc.uz.zgora.pl/publication/64287

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Edition name Date
On the robustness of learning in the multi-layer perceptron Sep 2, 2021

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