| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 404088 | Neural Networks | 2014 | 7 Pages |
Abstract
The Neocognitron and its related hierarchical models have been shown to be competitive in recognizing handwritten digits and objects. However, the tolerance of these models to several types of noise can be low. We will start by briefly overviewing some previous results regarding the tolerance of these models. Afterwards, we report the higher noise tolerance of the winner-take-all response in a hierarchical model over related models. We provide an analysis and interpretation of this tolerance under Bayesian decision theory. Finally, we report on how to further improve recognition for extremely noisy patterns.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ângelo Cardoso, Andreas Wichert,