Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs|
|Author:||De Chazal, P.|
|Citation:||2016 Intemational Joint Conference on Neural Networks (IJCNN), 2016 / vol.2016-October, pp.68-75|
|Series/Report no.:||IEEE International Joint Conference on Neural Networks (IJCNN)|
|Conference Name:||International Joint Conference on Neural Networks (IJCNN) (24 Jul 2016 - 29 Jul 2016 : Vancouver, Canada)|
|Philip de Chazal, Mark D. McDonnell|
|Abstract:||An efficient algorithm for the calculation of the approximate Hessian matrix for the Levenberg-Marquardt (LM) optimization algorithm for training a single-hidden-layer feedforward network with linear outputs is presented. The algorithm avoids explicit calculation of the Jacobian matrix and computes the gradient vector and approximate Hessian matrix directly. It requires approximately 1/N the floating point operations of other published algorithms, where N is the number of network outputs. The required memory for the algorithm is also less than 1/N of the memory required for algorithms explicitly computing the Jacobian matrix. We applied our algorithm to two large-scale classification problems - the MNIST and the Forest Cover Type databases. Our results were within 0.5% of the best performance of systems using pixel values as inputs to a feedforward network for the MNIST database. Our results were achieved with a much smaller network than other published results. We achieved state-of-the-art performance for the Forest Cover Type database.|
|Keywords:||Levenberg-Marquardt algorithm; feedforward neural networks; Gauss-Newton method; approximate Hessian calculation|
|Rights:||© 2016 IEEE|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.