The dipping phenomenon

M. Loog, R.P.W. Duin

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

19 Citations (Scopus)
3 Downloads (Pure)


One typically expects classifiers to demonstrate improved performance with increasing training set sizes or at least to obtain their best performance in case one has an infinite number of training samples at ones’s disposal. We demonstrate, however, that there are classification problems on which particular classifiers attain their optimum performance at a training set size which is finite. Whether or not this phenomenon, which we term dipping, can be observed depends on the choice of classifier in relation to the underlying class distributions. We give some simple examples, for a few classifiers, that illustrate how the dipping phenomenon can occur. Additionally, we speculate about what generally is needed for dipping to emerge. What is clear is that this kind of learning curve behavior does not emerge due to mere chance and that the pattern recognition practitioner ought to take note of it.
Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition (Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012. Proceedings)
EditorsG. Gimel'farb, E. Hancock, A. Imiya, A. Kuijper, M. Kudo, S. Omachi, T. Windeatt, K. Yamada
Place of PublicationBerlin
ISBN (Print)978-3-642-34165-6
Publication statusPublished - 2012
Externally publishedYes
EventJoint IAPR International Workshop SSPR+SPR, Hiroshima, Japan, November 7-9, 2012
- Hiroshima, Japan
Duration: 7 Nov 20129 Nov 2012

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceJoint IAPR International Workshop SSPR+SPR, Hiroshima, Japan, November 7-9, 2012
OtherJoint IAPR International Workshop SSPR+SPR 2012


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