Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
trNon-greedy active learning for text categorization using convex ansductive experimental design
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Uncertainty sampling and transductive experimental design for active dual supervision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Since labeling data is often both laborious and costly, the labeled data available in many applications is rather limited. Active learning is a learning approach which actively selects unlabeled data points to label as a way to alleviate the labeled data deficiency problem. In this paper, we extend a previous active learning method called transductive experimental design (TED) by proposing a new unlabeled data selection criterion. Our method, called discriminative experimental design (DED), incorporates both margin-based discriminative information and data distribution information and hence it can be seen as a discriminative extension of TED. We report experiments conducted on some benchmark data sets to demonstrate the effectiveness of DED.