Combining labeled and unlabeled data with co-training

19985,569 citationsJournal Articlegold Open Access

Authors

Tom M. Mitchell · Carnegie Mellon University

Abstract

We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available.

Topics & Keywords

Machine Learning and Algorithms Algorithms and Data Compression Machine Learning and Data Classification

UN Sustainable Development Goals

Partnerships for the goals

Publication Details

DOI: 10.1145/279943.279962

Field-Weighted Citation Impact: 31.87