Elements of information theory
Elements of information theory
Machine Learning
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
PAC Learning from Positive Statistical Queries
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
One-class svms for document classification
The Journal of Machine Learning Research
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to identify unexpected instances in the test set
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semi-supervised learning from only positive and unlabeled data using entropy
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Partially supervised classification – based on weighted unlabeled samples support vector machine
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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Traditional supervised classifiers use only labeled data (features/label pairs) as the training set, while the unlabeled data is used as the testing set. In practice, it is often the case that the labeled data is hard to obtain and the unlabeled data contains the instances that belong to the predefined class beyond the labeled data categories. This problem has been widely studied in recent years and the semi-supervised learning is an efficient solution to learn from positive and unlabeled examples(or PU learning). Among all the semi-supervised PU learning methods, it's hard to choose just one approach to fit all unlabeled data distribution. This paper proposes an automatic KL-divergence based semi-supervised learning method by using unlabeled data distribution knowledge. Meanwhile, a new framework is designed to integrate different semi-supervised PU learning algorithms in order to take advantage of the former methods. The experimental results show that (1)data distribution information is very helpful for the semi-supervised PU learning method; (2)the proposed framework can achieve higher precision when compared with the-state-of-the-art method.