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[R] A Growing Neural Gas Network Learns Topologies (NIPS 1994)

Abstract

An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the “neural gas” method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.

PDF: http://papers.nips.cc/paper/893-a-growing-neural-gas-network-learns-topologies.pdf

Interactive demo (2017): https://www.demogng.de/

submitted by /u/hardmaru
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.