A Fuzzy Measure for Recognition of Handwritten Letter Strokes

Wróbel, Michal R.; Nieszporek, Katarzyna; Starczewski, Janusz T.; Cader, Andrzej

doi:10.1007/978-3-319-91253-0_70

Cited by 6 publications

(2 citation statements)

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“…The Levenberg-Marquardt algorithm performs a weights update only once at the end of each epoch. At this moment, all elements of the Jacobian matrix are already calculated by using equations (6), (7), (8) and (9). For optimization of further calculations in the LM algorithm, it is assumed that S (w(n)) = µI.…”

Section: The Classic Levenberg-marquardt Algorithmmentioning

confidence: 99%

“…One of the most interesting areas of AI are neural networks. Each year researchers across the world produce an incredible number of scientific papers whose main theme originates from AI, especially from neural networks as in [1,2,3,4,5,6,7,8,9]. Industry strongly benefits from that by applying more and more advanced AI solutions in their products.…”

Section: Introductionmentioning

confidence: 99%

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Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Bilski

Kowalczyk

Marchlewska

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

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This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.

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Section: The Classic Levenberg-marquardt Algorithmmentioning

confidence: 99%