Most of the existing feature-based methods and graph-based methods require sufficiently large amount of labeled examples or a fully-labeled graph for training.
InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning.
Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.
We used very few labeled examples for initial training.
In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant.
Moreover, the number and especially the quality of the manually labeled examples challenge such strategies.
These learning strategies usually require a large set of labeled examples which can be expensive to obtain.
Using the same labeled examples for different scenarios might degrade the system performance.
Acquiring them as labeled examples will probably improve the model's detection capabilities.
Co-training first learns a separate classifier for each base learner using labeled examples.
