@inproceedings{eedff80eb79c400e817fde76ef50e3d9,
title = "The Peaking Phenomenon in Semi-supervised Learning",
abstract = "For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi-supervised setting, where instead of labeled objects, unlabeled objects are added to the training set. We explain why the learning curve has a more steep incline and a more gradual decline in this setting through simulation studies and by applying an approximation of the learning curve based on the work by Raudys and Duin.",
keywords = "Semi-supervised learning, Peaking, Least squares classfier, Pseudo-inverse",
author = "Jesse Krijthe and Marco Loog",
year = "2016",
doi = "10.1007/978-3-319-49055-7_27",
language = "English",
isbn = "978-3-319-49054-0",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "299--309",
editor = "A. Robles-Kelly and Marco Loog and B. Biggio and F. Escolano and R. Wilson",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",
note = "SSPR Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) ; Conference date: 29-11-2016 Through 02-12-2016",
}