Learning to Generalize from Single Examples in the Dynamic Link Architecture
Abstract
A large attraction of neural systems lies in their promise of replacing programming by learning. A problem with many current neural models is that with realistically large input patterns learning time explodes. This is a problem inherent in a notion of learning that is based almost entirely on statistical estimation. We propose here a different learning style where significant relations in the input pattern are recognized and expressed by the unsupervised self-organization of dynamic links. The power of this mechanism is due to the very general a priori principle of conservation of topological structure. We demonstrate that style with a system that learns to classify mirror symmetric pixel patterns from single examples.
Cite
Text
Konen and von der Malsburg. "Learning to Generalize from Single Examples in the Dynamic Link Architecture." Neural Computation, 1993. doi:10.1162/NECO.1993.5.5.719Markdown
[Konen and von der Malsburg. "Learning to Generalize from Single Examples in the Dynamic Link Architecture." Neural Computation, 1993.](https://mlanthology.org/neco/1993/konen1993neco-learning/) doi:10.1162/NECO.1993.5.5.719BibTeX
@article{konen1993neco-learning,
title = {{Learning to Generalize from Single Examples in the Dynamic Link Architecture}},
author = {Konen, Wolfgang and von der Malsburg, Christoph},
journal = {Neural Computation},
year = {1993},
pages = {719-735},
doi = {10.1162/NECO.1993.5.5.719},
volume = {5},
url = {https://mlanthology.org/neco/1993/konen1993neco-learning/}
}