摘要：To select unlabeled example effectively and reduce classification error, confidence estimation for graph-based semi-supervised learning （CEGSL） is proposed. This algorithm combines graph-based semi-supervised learning with collaboration-training. It makes use of structure information of sample to calculate the classification probability of unlabeled example explicitly. With multi-classifiers, the algorithm computes the confidence of unlabeled example implicitly. With dual-confidence estimation, the unlabeled example is selected to update classifiers. The comparative experiments on UCI datasets indicate that CEGSL can effectively exploit unlabeled data to enhance the learning performance.
