Efficient Learning from Few Labeled Examples

Authors:
Jiao Wang;Siwei Luo;Jingjing Zhong
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 100044;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 100044;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 100044
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

Citing 8
Cited 0

Selective Sampling Using the Query by Committee Algorithm

Machine Learning
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Active + Semi-supervised Learning = Robust Multi-View Learning

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-Supervised Learning on Riemannian Manifolds

Machine Learning
On multi-view active learning and the combination with semi-supervised learning

Proceedings of the 25th international conference on Machine learning
Active learning with multiple views

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Active learning and semi-supervised learning are two approaches to alleviate the burden of labeling large amounts of data. In active learning, user is asked to label the most informative examples in the domain. In semi-supervised learning, labeled data is used together with unlabeled data to boost the performance of learning algorithms. We focus here to combine them together. We first introduce a new active learning strategy, then we propose an algorithm to take the advantage of both active learning and semi-supervised learning. We discuss several advantages of our method. Experimental results show that it is efficient and robust to noise.