PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Taxonomy for Semi-Supervised Learning Methods
matthias seeger
In: Semi-Supervised Learning (2006) MIT Press , pp. 15-32.

Abstract

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.

EPrint Type:Book Section
Additional Information:Available at http://www.kyb.tuebingen.mpg.de/bs/people/seeger/
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2698
Deposited By:matthias seeger
Deposited On:19 December 2007