Neural Computation
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
Watch, Listen & Learn: Co-training on Captioned Images and Videos
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Semi-supervised learning by disagreement
Knowledge and Information Systems
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
The value of agreement, a new boosting algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
CoTrade: Confident Co-Training With Data Editing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Scalable k-NN graph construction for visual descriptors
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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We propose a new multi-view semi-supervised learning method named co-graph for web image classification. Co-graph combines multiple graphs together, each modeling the data points on one view, and enhances learners incrementally with co-training strategy by exploiting unlabeled data. Learners are locally co-trained in co-graph for the enhancement, i.e., only a part of local models in graphs, named dominant local models, need to be updated instead of the total. We also extend the co-graph algorithm to a general framework of local co-training over multiple graphs that is compatible with the common graph-based learning algorithms. Co-graph builds a bridge between graph-based methods and co-training, and contains the double label propagation: one propagates labels from labeled data to unlabeled data in each single view, and the other exchanges high-confidence label information across different views. Experimental results demonstrate the effectiveness of co-graph in web image classification.