UNSUPERVISED LEARNING

The best results for unsupervised learning are, currently, from my MK10 and SNPR models of first language learning. An account of this work, and copies of papers, may be found in Language Learning as Compression.

SP70 (described below) is a relatively new model of unsupervised learning that attempts to integrate learning with such things as parsing and production of language, fuzzy pattern recognition and best-match information retrieval, probabilistic and exact forms of reasoning, and others. As a model of learning, it is not yet as successful as the earlier models.

Developing multi-level grammars in a framework of information compression by multiple alignment, unification and search

Cognition Research Technical Report, March 2005. PDF.

This paper describes the SP71 model of grammatical inference designed to overcome the weakness in SP70, that it is only able to discover structure at two levels of abstraction, words and sentences. By contrast, SP71 is able to discover intermediate levels of structure such as phrases and clauses. More work is needed to iron out some anomalies in the model.

Unsupervised learning in a framework of information compression by multiple alignment, unification and search

School of Informatics Report, November 2001, University of Wales Bangor. PDF, Postscript, uk.arxiv.org/abs/cs.AI/0302015.

Describes SP70 (version 9.2), a development of the SP framework that incorporates unsupervised learning of grammar-like structures from language-like input. A short version of this paper was presented at the Workshop and Tutorial on Learning Context-Free Grammars at ECML/PKDD2003.

Last updated: 2006-05-22 (ISO 8601)