Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
IEEE Transactions on Pattern Analysis and Machine Intelligence
The DSFPN, a new neural network for optical character recognition
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Semi-supervised classification uses a large amount of unlabeled data to help a little amount of labeled data for designing classifiers, which has good potential and performance when the labeled data are difficult to obtain. This paper mainly discusses semi-supervised classification based on CPN (Counter-propagation Network). CPN and its revised models have merits such as simple structure, fast training and high accuracy. Especially, its training scheme combines supervised learning and unsupervised learning, which makes it very conformable to be extended to semi-supervised classification problem. According to the characteristics of CPN, we propose a semi-supervised dynamic CPN, and compare it with other two semi-supervised CPN models using Expectation Maximization and Co-Training/Self-Training techniques respectively. The experimental results show the effectiveness of CPN based semi-supervised classification methods.