Theory of Self-Organizing Nerve Nets with Special Reference to Association and Concept Formation
Abstract
The present paper proposes a mathematical theory of self-organizing nerve nets, which is applicable to various types of supervised and unsupervised learning, such as learning decision, concept formation, association, etc. Given an environmental information source, a neural system automatically forms a number of separate routines to process the signals in it. This kind of unsupervised self-organization underlies commonly in formation of categories, feature extractors, and content addressable memories. This problem is analyzed mathematically, as well as models of topographic organization of nerve fields and of associative memories, by the proposed method.
Cite
Text
Amari. "Theory of Self-Organizing Nerve Nets with Special Reference to Association and Concept Formation." International Joint Conference on Artificial Intelligence, 1979.Markdown
[Amari. "Theory of Self-Organizing Nerve Nets with Special Reference to Association and Concept Formation." International Joint Conference on Artificial Intelligence, 1979.](https://mlanthology.org/ijcai/1979/amari1979ijcai-theory/)BibTeX
@inproceedings{amari1979ijcai-theory,
title = {{Theory of Self-Organizing Nerve Nets with Special Reference to Association and Concept Formation}},
author = {Amari, Shun-ichi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1979},
pages = {13-15},
url = {https://mlanthology.org/ijcai/1979/amari1979ijcai-theory/}
}