Algorithm design
Co-training is a semi-supervised learning technique that requires two views of the data. It assumes that each example is described using two different sets of features that provide complementary information about the instance.
This paper gives a review on co-training style algorithms just from this view and presents typical examples and analysis for each level respectively. Published ...
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In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given ...
In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given ...
In this paper, a co-training style semi-supervised regression algo- rithm, i.e. COREG, is proposed. This algorithm uses two k-nearest neighbor regressors with ...
It is shown that the co- training process can succeed even without two views, given that the two learners have large difference, which explains the success ...
To optimize and solve the shortcomings of traditional Co-training style algorithms, a novel Co-training method based on sub-Kmeans named Op-FSCO is proposed.
Co-training style algorithms follow the iterative learning process of co-training but formu- late different schemes to select unlabeled instances. Goldman and ...
In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given ...
Jan 24, 2024 · Co-training is a semisupervised learning technique that capitalizes on the concept of learning from multiple, potentially complementary, views of the data.
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