Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

Ting, Kai Ming, Washio, Takashi, Wells, Jonathan R. and Aryal, Sunil 2017, Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors, Machine learning, vol. 106, no. 1, pp. 55-91, doi: 10.1007/s10994-016-5586-4.

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Title Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors
Author(s) Ting, Kai Ming
Washio, Takashi
Wells, Jonathan R.
Aryal, SunilORCID iD for Aryal, Sunil orcid.org/0000-0002-6639-6824
Journal name Machine learning
Volume number 106
Issue number 1
Start page 55
End page 91
Total pages 37
Publisher Springer
Place of publication New York, N.Y.
Publication date 2017
ISSN 1573-0565
Summary Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as ‘more data the better’. We call this ‘the gravity of learning curve’, and it is assumed that no learning algorithms are ‘gravity-defiant’. Contrary to the conventional wisdom, this paper provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms.
Language eng
DOI 10.1007/s10994-016-5586-4
Field of Research 0801 Artificial Intelligence and Image Processing
1702 Cognitive Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2016, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121092

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