Publications
Authors:
  • Eric Gaussier , Cyril Goutte
Citation:
Proceedings of Learning with Partially Classified Training Data - ICML 2005 workshop, Bonn, Germany, 7 August, 2005.
Abstract:
In this paper, we propose a unifying treatment of several strategies for training mixture models from label-deficient data. After a review of different approaches to estimating classification models on partially labelled data using mixture models, we identify a number of problems which lead us to propose a new EM variant. The aim is to better handle unlabelled data and provide a more confident discrimination decision. This is illustrated by an experimental comparison of the different models on the Leptograpsus crab data.
Year:
2005
Report number:
2005/030
Attachments: