Semi-Supervised Learning -- A Statistical Physics Approach

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dc.creatorGetz, Gad
dc.creatorShental, Noam
dc.creatorDomany, Eytan
dc.date2006-04-05
dc.date2006-04-06
dc.date.accessioned2026-07-07T07:09:19Z
dc.date.available2026-07-07T07:09:19Z
dc.descriptionWe present a novel approach to semi-supervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal k-way cut solution. In contrast to these methods, we estimate the distribution of classifications, instead of the sole minimal k-way cut, which yields more accurate and robust results. Our approach may be applied to all energy functions used for semi-supervised learning. The method is based on sampling using a Multicanonical Markov chain Monte-Carlo algorithm, and has a straightforward probabilistic interpretation, which allows for soft assignments of points to classes, and also to cope with yet unseen class types. The suggested approach is demonstrated on a toy data set and on two real-life data sets of gene expression.
dc.description9 pages. Appeared in in the Proceedings of "Learning with Partially Classified Training Data", ICML 2005 workshop
dc.identifierhttps://arxiv.org/abs/cs/0604011
dc.identifierhttp://arxiv.org/abs/cs/0604011
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/111016
dc.subjectMachine Learning
dc.subjectStatistical Mechanics
dc.subjectComputer Vision and Pattern Recognition
dc.titleSemi-Supervised Learning -- A Statistical Physics Approach
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