.png){kind=icon}
返回
Manifold proximal support vector machine for semi-supervised classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-013-0491-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, semi-supervised learning (SSL) has attracted a great deal of attention in the machine learning community. Under SSL, large amounts of unlabeled data are used to assist the learning procedure to construct a more reasonable classifier. In this paper, we propose a novel manifold proximal support vector machine (MPSVM) for semi-supervised classification. By introducing discriminant information in the manifold regularization (MR), MPSVM not only introduces MR terms to capture as much geometric information as possible from inside the data, but also utilizes the maximum distance criterion to characterize the discrepancy between different classes, leading to the solution of a pair of eigenvalue problems. In addition, an efficient particle swarm optimization (PSO)-based model selection approach is suggested for MPSVM. Experimental results on several artificial as well as real-world datasets demonstrate that MPSVM obtains significantly better performance than supervised GEPSVM, and achieves comparable or better performance than LapSVM and LapTSVM, with better learning efficiency.
Keyword:
Semi-supervised classification
Manifold regularization
Support vector machine
Nonparallel hyperplanes
Particle swarm optimization
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Particle swarm optimization for parameter determination and feature selection of support vector machines粒子群优化算法在支持向量机参数确定与特征选择中的应用
PREAI
分享收藏Transition dipole interaction in polypeptides: Ab initio calculation of transition dipole parameters
OAAI
OAAI分享收藏
查看更多内容