Abstract
A trust-region-based error-aggregated training algorithm (TREAT) for multi-layer feedforward neural networks is proposed. In the same spirit as that of the Levenberg-Marquardt (LM) method, the TREAT algorithm uses a different Hessian matrix approximation, which is based on the Jacobian matrix derived from aggregated errors. An aggregation scheme is discussed. It can greatly reduce the size of the matrix to be inverted in each training iteration and thereby lower the iterative computational cost. Compared with the LM method, the TREAT algorithm is computationally less intensive, and requires less memory. This is especially important for large sized neural networks where the LM algorithm becomes impractical.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
