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
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multi-model similarity propagation and its application for web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
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
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
Learning Image-Text Associations
IEEE Transactions on Knowledge and Data Engineering
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 with multiple views
Semi-supervised learning with multiple views
A parametric methodology for text classification
Journal of Information Science
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The value of agreement, a new boosting algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
From graphs to manifolds – weak and strong pointwise consistency of graph laplacians
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
Editorial: Partially supervised learning for pattern recognition
Pattern Recognition Letters
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Web data, such as web pages and web images, can be naturally partitioned into multiple heterogeneous attribute sets. Concretely speaking, web pages consist of hyperlink and contents, and web images consist of the textual and visual information. In this paper, we propose a new multi-view semi-supervised learning method, named local co-training, for web page and image classification. Local co-training employs local linear models to represent data points on each view (i.e. one attribute set), and iteratively refines them using unlabelled data with co-training strategy. In each iteration, only a part of local models that we call dominant local models needs to be incrementally updated. The method is thus efficient and fit for the learning of large-scale web data. In addition, we introduce a new measurement based on both the confidence and the disagreement to describe which unlabelled examples are 'good' for the enrichment of training sets. Local co-training builds a bridge between two dominant types of semi-supervised methods: graph-based methods and co-training. Experiments on web page and web image datasets demonstrate that local co-training can effectively improve the classification performance by exploiting multiple attribute sets and unlabelled data.