A Taxonomy for Semi-Supervised Learning Methods

We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature. Finally, we shed more detailed light on the family of methods using input-dependent regularization (or conditional prior distributions) and show parallels to the Co-training paradigm.


Editor(s):
Chapelle, O.
Schoelkopf, Bernhard
Zien, A.
Published in:
Semi-Supervised Learning, 15-32
Year:
2006
Publisher:
MIT Press
Keywords:
Laboratories:




 Record created 2010-12-02, last modified 2018-03-17

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