This paper addresses the issues of global optimality and training of a Feedforward Neural Network (FNN) error funtion incoporating the weight decay regularizer. A network with a single hidden-layer and a single output-unit is considered. Explicit vector and matrix canonical forms for the Jacobian and Hessian of the network are presented. Convexity analysis is then performed utilizing the known canonical structure of the Hessian. Next, global optimality characterization of the FNN error function is attempted utilizing the results of convex characterization and a convex monotonic transformation. Based on this global optimality characterization, an iterative algorithm is proposed for global FNN learning. Numerical experiments with benchmark examples show better convergence of our network learning as compared to many existing methods in the literature. The network is also shown to generalize well for a face recognition problem.
|Title of host publication||Energy Minimization Methods in Computer Vision and Pattern Recognition - 3rd International Workshop, EMMCVPR 2001, Proceedings|
|Editors||Anil K. Jain, Mario Figueiredo, Josiane Zerubia|
|Number of pages||16|
|ISBN (Print)||3540425233, 9783540425236|
|Publication status||Published - 2001|
|Event||3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001 - Sophia Antipolis, France|
Duration: 2001 Sept 3 → 2001 Sept 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001|
|Period||01/9/3 → 01/9/5|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)