Analyzing Co-training Style Algorithms

By Wei Wang, Zhi-Hua Zhou. 2007

233 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

A Survey on Multi-view Learning (2013, background): “Wang and Zhou (2007) studied why co-training style algorithms can A Survey on Multi-view Learning succeed when there are no redundant views.”
Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020, background): “Classical approaches can be broadly categorized in co-training procedures [6, 7, 30, 43, 50, 67, 84] that maximize the mutual agreement between views, multiple kernel learning procedures [5, 40, 44] which use kernels to model different views, and subspace learning procedures [15, 68] which seek to f”
Deep Transfer Learning for Person Re-Identification (2016, methodology): “To address the issue of model drift, co-training [23], [24] is used in our model.”
Diversity-induced Multi-view Subspace Clustering (2015, background): “Furthermore, some theoretical results [4, 5, 26] have shown that the independence of different views can serve as a helpful complement to the multi-view learning.”
Multi-View Intact Space Learning (2015, background): “After that, Wang and Zhou conducted a series of in-depth analyses and revealed some interesting properties of co-training, including the large-diversity of classiﬁers [18], [19], label propagation over two views [20] and co-training with insufﬁcient views [21].”
Combining Committee-Based Semi-Supervised Learning and Active Learning (2010, background): “Li and Zhou[2] proposed an extension to TriTraining, called Co-Forest, in which an initial ensemble of random trees is trained on bootstrap subsamples generated from the given labeled dataset L.”
Multi-view clustering: A survey (2018, unknown): “Thus, several weaker assumptions such as the weak conditional dependence assumption[20], much weaker “expansion” assumption[21], and the difference assumption[22] have been investigated.”
Inductive multi-task learning with multiple view data (2012, background): “Multi-view semi-supervised learning has attracted significant research interest in recent years [1, 5, 11, 14, 19, 23, 24].”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “Wang and Zhou [48] demonstrated that the performance of co-training algorithms was largely affected by the complementary information in distinct views.”
Multi-view learning overview: Recent progress and new challenges (2017, background): “[53] W. Wang , Z. Zhou , A new analysis of co-training, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 1135–1142 .”
A survey of multi-view machine learning (2013, background): “Wang and Zhou [48] proved that the co-training process can succeed even without two views, given that the labeled data set is sufficient to learn good classifiers and the two classifiers have a large diversity.”
A survey on semi-supervised learning (2019, methodology): “Wang and Zhou (2007) provided both theoretical and empirical analyses on why co-training can work in single-view settings.”