TITLE: Design of Robust Neural Network Classifiers

AUTHORS: Jan Larsen, Lars Nonboe Andersen, Mads Hintz-Madsen and Lars Kai Hansen
connect, Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
emails: jl,lna,mhm,lkhansen@imm.dtu.dk
www: http://eivind.imm.dtu.dk


This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present a modified likelihood function which incorporate the potential risk of outliers in the data. This leads to introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We suggest to adapt the outlier probability and regularization parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrates the potential of the suggested framework.

In Proceedings of ICASSP'98, Seattle, WA, May 12-15, 1998.