The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c ©

413 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Review of Deep Learning Algorithms and Architectures (2019, methodology): “Gradient descent is employed to reduce the sum of the squared errors by updating the parameters of the function in the direction of the steepestdescent, while the Gauss-Newtonmethodminimizes the error by assuming the function to be locally quadratic and finds the minimum of the quadratic [49].”
Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation (2013, methodology): “The LMmethod acts more like a gradient-descent method when the parameters are far from their optimal value and acts more like the Gauss–Newton method when the parameters are close to their optimal value [29].”
Neural Networks and Deep Learning (2018, unknown): “Overviews of the approach may be found in [133, 300].”