Adapting to drift in continuous domains (Extended abstract)
Lecture notes in computer sciencePublished 1 January 1995
Miroslav Kubát, Gerhard Widmer
Citations32
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
The experiments demonstrate that FRANN compares favourably with FLORA4 in the presence of concept drift, and is particularly effective in capturing concepts with nonlinear boundaries.
Abstract
The experiments demonstrate that FRANN compares favourably with FLORA4 in the presence of concept drift. Learning is possible from examples described by symbolic as well as by numeric attributes, and because of its representation formalism (RBF networks, which realize a kind of prototype weighting scheme) FRANN is particularly effective in capturing concepts with nonlinear boundaries.
Keywords
Computer Science
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