A comparative study of optimization techniques for backpropagation

Barnard, Etienne; Holm, Johann

doi:10.1016/0925-2312(94)90031-0

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…This for example cannot be visualized in a single regressions formula. The classification performance of ANNs is constantly being improved (Barnard and Holm, 1994;Alpsan et al, 1995;Looney, 1996;Orr and Müller, 1998). It is in this sense, that the learning algorithm adaptive propagation (APROP; was developed and implemented in the software ACMD (Approximation and Classification of Medical Data).…”

Section: Introductionmentioning

confidence: 99%

The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

et al. 2004

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Section: Introductionmentioning

confidence: 99%

The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

et al. 2004

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Computer Vision for Automated Quality Inspection of Colour Printing Products

Jian

Zhang

Ismail

2000

Proceedings of the 33rd International MATADOR Conference

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Microarray Data Classified by Artificial Neural Networks

Linder

Richards

Wagner

2007

Methods in Molecular Biology

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Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis of high-throughput data as provided by DNA or protein microarrays. Along with the increasing availability of microarray data, attention is focused on methods of analyzing the expression rates. One important type of analysis is the classification task, for example, distinguishing different types of cell functions or tumors. Recently, interest has been awakened toward artificial neural networks (ANN), which have many appealing characteristics such as an exceptional degree of accuracy. Nonlinear relationships or independence from certain assumptions regarding the data distribution are also considered. The current work reviews advantages as well as disadvantages of neural networks in the context of microarray analysis. Comparisons are drawn to alternative methods. Selected solutions are discussed, and finally algorithms for the effective combination of multiple ANNs are presented. The development of approaches to use ANN-processed microarray data applicable to run cell and tissue simulations may be slated for future investigation.

show abstract

A comparative study of optimization techniques for backpropagation

Cited by 11 publications

References 16 publications

The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

Computer Vision for Automated Quality Inspection of Colour Printing Products

Microarray Data Classified by Artificial Neural Networks

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