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1997 | OriginalPaper | Buchkapitel

Application of the Constructive Mikado-Algorithm on Remotely Sensed Data

verfasst von : C. Cruse, S. Leppelmann, A. Burwick, M. Bode

Erschienen in: Neurocomputation in Remote Sensing Data Analysis

Verlag: Springer Berlin Heidelberg

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Finding an optimal architecture for a neural network, i.e. an optimal number and size of layers, is an open problem. With the Mikado-algorithm we present a new method to construct the network architecture in the course of learning. With two examples, classification and structure detection from remotely sensed data, we demonstrate the capabilities of the Mikado-algorithm. This algorithm provides good generalisation in the presence of mixed pixels, delivering small networks for high-dimensional problems and a new way of interpreting the network generalisation ability.

Metadaten
Titel
Application of the Constructive Mikado-Algorithm on Remotely Sensed Data
verfasst von
C. Cruse
S. Leppelmann
A. Burwick
M. Bode
Copyright-Jahr
1997
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-59041-2_20

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