The nature of statistical learning theory
The nature of statistical learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Classifier learning with a new locality regularization method
Pattern Recognition
Classifier learning with a new locality regularization method
Pattern Recognition
A second order cone programming approach for semi-supervised learning
Pattern Recognition
| Hi-index | 0.01 |
We study the problem of image retrieval based on semi-supervised learning. Semi-supervised learning has attracted a lot of attention in recent years. Different from traditional supervised learning. Semi-supervised learning makes use of both labeled and unlabeled data. In image retrieval, collecting labeled examples costs human efforts, while vast amounts of unlabeled data are often readily available and offer some additional information. In this paper, based on support vector machine (SVM), we introduce a semi-supervised learning method for image retrieval. The basic consideration of the method is that, if two data points are close to each, they should share the same label. Therefore, it is reasonable to search a projection with maximal margin and locality preserving property. We compare our method to standard SVM and transductive SVM. Experimental results show efficiency and effectiveness of our method.