An overview on semi-supervised support vector machine

作者： | Shifei Ding, Zhibin Zhu, Xiekai Zhang |

作者单位： |
^{1}China University of Mining and Technology |

刊名： | Neural Computing and Applications, 2017, Vol.28 (5), pp.969-978 |

来源数据库： | Springer Nature Journal |

DOI： | 10.1007/s00521-015-2113-7 |

关键词： | Semi-supervised; Support vector machine; Semi-supervised support vector machine; |

英文摘要： | Support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies to supervised learning. Large amount of data generated in real life is unlabeled, and the standard form of SVM cannot make good use of these data to improve its learning ability. However, semi-supervised support vector machine (S3VM) is a good solution to this problem. This paper reviews the recent progress in semi-supervised support vector machine. First, the basic theory of S3VM is expounded and discussed in detail; then, the mainstream model of S3VM is presented, including transductive support vector machine,... |

原始语种摘要： | Support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies to supervised learning. Large amount of data generated in real life is unlabeled, and the standard form of SVM cannot make good use of these data to improve its learning ability. However, semi-supervised support vector machine (S3VM) is a good solution to this problem. This paper reviews the recent progress in semi-supervised support vector machine. First, the basic theory of S3VM is expounded and discussed in detail; then, the mainstream model of S3VM is presented, including transductive support vector machine,... |