Robust MCD-Based Backpropagation Learning Algorithm View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008-01-01

AUTHORS

Andrzej Rusiecki

ABSTRACT

Training data containing outliers are often a problem for supervised neural networks learning methods that may not always come up with acceptable performance. In this paper a new, robust to outliers learning algorithm, employing the concept of initial data analysis by the MCD (minimum covariance determinant) estimator, is proposed. Results of implementation and simulation of nets trained with the new algorithm and the traditional backpropagation (BP) algorithm and robust Lmls are presented and compared. The better performance and robustness against outliers for the new method are demonstrated. More... »

PAGES

154-163

Book

TITLE

Artificial Intelligence and Soft Computing – ICAISC 2008

ISBN

978-3-540-69572-1
978-3-540-69731-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-69731-2_16

DOI

http://dx.doi.org/10.1007/978-3-540-69731-2_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025709181


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