Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search
人工智能
2007-05-23 v1 机器学习
摘要
This paper describes a novel approach to unsupervised learning that has been developed within a framework of "information compression by multiple alignment, unification and search" (ICMAUS), designed to integrate learning with other AI functions such as parsing and production of language, fuzzy pattern recognition, probabilistic and exact forms of reasoning, and others.
引用
@article{arxiv.cs/0302015,
title = {Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search},
author = {J. G. Wolff},
journal= {arXiv preprint arXiv:cs/0302015},
year = {2007}
}
备注
39 pages, 1 JPEG figure