PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-supervised Learning with an Imperfect Supervisor
Massih Amini and Patrick Gallinari
Knowledy and Information Systems 2005. ISSN 0219-1377


Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems. This paper describes a new discriminant algorithm for semi-supervised learning. This algorithm optimizes the classification maximum likelihood (CML) of a set of labeled–unlabeled data, using a discriminant extension of the Classification Expectation Maximization algorithm.We further propose to extend this algorithm by modeling imperfections in the estimated class labels for unlabeled data. The parameters of this labelerror model are learned together with the semi-supervised classifier parameters. We demonstrate the effectiveness of the approach using extensive experiments on different datasets.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:1061
Deposited By:Massih Amini
Deposited On:04 September 2005