A second order cone programming approach for semi-supervised learning

作者：

Highlights：

• We proposed a novel algorithm for semi-supervised learning.

• The algorithm is based on the multiple clusters per class assumption.

• It combines the efficient kNN method with a maximal margin classifier.

• It is efficient and leads to competitive results compared to state-of-the-art algorithms.

摘要

Highlights•We proposed a novel algorithm for semi-supervised learning.•The algorithm is based on the multiple clusters per class assumption.•It combines the efficient kNN method with a maximal margin classifier.•It is efficient and leads to competitive results compared to state-of-the-art algorithms.

论文关键词：Semi-supervised learning,K-nearest-neighbor,Support vector machine,Second order cone programming

论文评审过程：Received 8 July 2012, Revised 9 May 2013, Accepted 16 June 2013, Available online 26 June 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.06.016