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Sequential non-stationary dynamic classification with sparse feedback

Abstract:

Many data analysis problems require robust tools for discerning between states or classes in the data. In this paper we consider situations in which the decision boundaries between classes are potentially non-linear and subject to "concept drift" and hence static classifiers fail. The applications for which we present results are characterized by the requirement that robust online decisions be made and by the fact that target labels may be missing, so there is very often no feedback regarding...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Garnett, R More by this author
Journal:
PATTERN RECOGNITION
Volume:
43
Issue:
3
Pages:
897-905
Publication date:
2010-03-05
DOI:
ISSN:
0031-3203
URN:
uuid:46cb7603-7f10-453a-9123-75414578d652
Source identifiers:
63679
Local pid:
pubs:63679

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