Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training process ...
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as containing two distinct sets of features. It has had a number of practical succ...
The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algo...
Co-training is a semi-supervised technique that allows classifiers to learn with fewer labelled documents by taking advantage of the more abundant unclassified documents. However, ...
We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumption...
When dealing with information overload from the Internet, such as the classification of Web pages and the filtering of email spam, a new technique called cotraining has been shown...
This paper explores collaborative ability of co-training algorithm. We propose a new measurement (CA) for representing the collaborative ability of co-training classifiers based o...
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, Chew L...
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. A major obstacle to this is the insufficiency o...
Training a good text detector requires a large amount of labeled data, which can be very expensive to obtain. Cotraining has been shown to be a powerful semi-supervised learning t...