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https://hdl.handle.net/20.500.11851/10999| Title: | A Modified Levenberg Marquardt Algorithm for Simultaneous Learning of Multiple Datasets | Authors: | Efe, M.O. Kurkcu, B. Kasnakoglu, C. Mohamed, Z. Liu, Z. |
Keywords: | Artificial neural networks; Biological neural networks; Jacobian matrices; Levenberg-Marquardt Algorithm; Masked Neural Networks; Multiple Dataset Learning; Neurons; Optimization; Training; Tuning Bioinformatics; DNA; Jacobian matrices; Learning algorithms; Parameter estimation; Best paths; Biological neural networks; Levenberg-Marquardt algorithm; Masked neural network; Multiple data sets; Multiple dataset learning; Neural-networks; Optimisations; Training dataset; Tuning; Neural networks |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This paper demonstrates the use of LM optimization algorithm when there are more than one dataset and on/off type switching of NN parameters is allowed. For each dataset a pre-selected set of parameters are allowed for modification and the proposed scheme reformulates the Jacobian under the switching mechanism. The results show that a NN can store information available in different datasets by a simple modification to the original LM algorithm, which is the novelty introduced in this study. The results are verified on a regression problem. IEEE | URI: | https://doi.org/10.1109/TCSII.2023.3335140 https://hdl.handle.net/20.500.11851/10999 |
ISSN: | 1549-7747 |
| Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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