Hierarchical growing neural gas

K. Doherty; R.G. Adams; N. Davey

doi:10.1007/3-211-27389-1_34

Hierarchical growing neural gas

K. Doherty, R.G. Adams, N. Davey

Centre for Computer Science and Informatics Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy.

Original language	English
Title of host publication	05)
Publisher	Springer Nature Link
Pages	140-143
ISBN (Print)	978-3-211-24934-5
DOIs	https://doi.org/10.1007/3-211-27389-1_34
Publication status	Published - 2005

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title = "Hierarchical growing neural gas",

abstract = "This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy.",

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AB - This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy.

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Hierarchical growing neural gas

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