Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

Image classification is a key task in image understanding, and multi-label image
classification has become a popular topic in recent years. However, the success of multi …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Active learning aims to train an accurate prediction model with minimum cost by labeling
most informative instances. In this paper, we survey existing works on active learning from …

In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …

The balanced weighting scheme of tree lasso and additional experimental results. We prove
that the weighting scheme of the tree-lasso penalty achieves a balanced penalization of all …

Active learning methods which present selected examples from the corpus for annotation
provide more efficient learning of supervised relation extraction models, but they leave the …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

Corpus based approaches to automatic translation such as Example Based and Statistical
Machine Translation systems use large amounts of parallel data created by humans to train …

Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …