Machine Learning
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Semi-supervised co-training and active learning based approach for multi-view intrusion detection
Proceedings of the 2009 ACM symposium on Applied Computing
Fault diagnosis of power transformer based on support vector machine with genetic algorithm
Expert Systems with Applications: An International Journal
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Graph-Based Semi-Supervised Learning and Spectral Kernel Design
IEEE Transactions on Information Theory
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Currently, condition-based maintenance becomes increasingly important with additions of factory automation through the development of new technologies. For many complicated machines, it is difficult to use mathematical models to describe their conditions. Intelligent maintenance makes it possible to perform maintenance similar to that of a human being. However, conventional artificial intelligent methods such as neural network and SVM use only labeled data (feature/label pairs) for training. Labeled instances are often difficult, expensive, or time consuming to obtain. Active learning and semi-supervised learning address this problem by using a large amount of unlabeled data together with labeled data to build better models. In this paper, a new active semi-supervised procedure was proposed to perform fault classification for machine condition monitoring. The effectiveness of the procedure was verified by its application to bearing diagnosis and gear fault detection.