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Regularized Boost for Semi-supervised Ranking
[chapter]
2015
Proceedings in Adaptation, Learning and Optimization
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smoothness regularizer to semi-supervised boosting algorithms based on the universal optimization
doi:10.1007/978-3-319-13359-1_49
fatcat:4mafb7jwnzcbtoz4an5hrytkdq