Regularized Boost for Semi-supervised Ranking [chapter]

Zhigao Miao, Juan Wang, Aimin Zhou, Ke Tang
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
more » ... k of margin cost functionals. Our regularizer is applicable to existing semi-supervised boosting algorithms to improve their generalization and speed up their training. Comparative results on synthetic, benchmark and real world tasks demonstrate the effectiveness of our local smoothness regularizer. We discuss relevant issues and relate our regularizer to previous work.
doi:10.1007/978-3-319-13359-1_49 fatcat:4mafb7jwnzcbtoz4an5hrytkdq