On Speeding up the Levenberg-Marquardt Learning Algorithm
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14125 LNAI, Page: 12-22
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
A new approach to the practical realizations of calculations to the Levenberg-Marquardt learning algorithm is presented. The proposed solutions aim to effectively reduce the high computational load of the LM algorithm. The detailed application of proposed methods in the process of learning neural networks is explicitly discussed. Experimental results have been obtained for all proposed methods and they confirm a very good performance of them.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85172415799&origin=inward; http://dx.doi.org/10.1007/978-3-031-42505-9_2; https://link.springer.com/10.1007/978-3-031-42505-9_2; https://dx.doi.org/10.1007/978-3-031-42505-9_2; https://link.springer.com/chapter/10.1007/978-3-031-42505-9_2
Springer Science and Business Media LLC
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