Invariance and neural nets

Authors:
E. Barnard;D. Casasent
Affiliations:
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA;-
Venue:
IEEE Transactions on Neural Networks
Year:
1991

Citing 0
Cited 4

Modified high-order neural network for invariant pattern recognition

Pattern Recognition Letters
A hierarchical learning network for face detection with in-plane rotation

Neurocomputing
A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions

Information Sciences: an International Journal
Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Application of neural nets to invariant pattern recognition is considered. The authors study various techniques for obtaining this invariance with neural net classifiers and identify the invariant-feature technique as the most suitable for current neural classifiers. A novel formulation of invariance in terms of constraints on the feature values leads to a general method for transforming any given feature space so that it becomes invariant to specified transformations. A case study using range imagery is used to exemplify these ideas, and good performance is obtained