We present a method to use multilayer perceptrons (MLPs) for a verification task, i.e. to verify whether two vectors are from the same class or not. In tests with synthetic data we could show that the verification MLPs are almost optimal from a Bayesian point of view. With speech data we have shown that verification MLPs generalize well such that they can be deployed as well for classes which were not seen during the training.
Cite as: Gerber, M., Kaufmann, T., Pfister, B. (2007) Perceptron-based class verification. Proc. ITRW on Nonlinear Speech Processing (NOLISP 2007), 35-38
@inproceedings{gerber07_nolisp,
author={Michael Gerber and Tobias Kaufmann and Beat Pfister},
title={{Perceptron-based class verification}},
year=2007,
booktitle={Proc. ITRW on Nonlinear Speech Processing (NOLISP 2007)},
pages={35--38}
}