In some classification tasks, such as those related to the automatic building and maintenance of text corpora, it is expensive to obtain labeled examples to train a classifier. In such circumstances it is common to have massive corpora where a few examples are labeled (typically a minority) while others are not. Semi-supervised learning techniques try to leverage the intrinsic information in un...

Graph-based methods are very popular in semi-supervised learning due to their well founded theoretical background, intuitive interpretation of local neighborhood structure, and strong performance on a wide range of challenging learning problems. However, the success of these methods is highly dependent on the pre-existing neighborhood structure in the data used to construct the graph. In this p...

There has recently been a large effort in using unlabeled data in conjunction with labeled data in machine learning. Semi-supervised learning and active learning are two well-known techniques that exploit the unlabeled data in the learning process. In this work, the active learning is used to query a label for an unlabeled data on top of a semisupervised classifier. This work focuses on the que...

Acquiring labels for large datasets can be a costly and timeconsuming process. This has motivated the development of the semisupervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning...

In order to solve the difficult questions such as in the presence of the cluster deviation and high dimensional data processing in traditional semi-supervised clustering algorithm, a semi-supervised clustering algorithm based on active learning was proposed, this algorithm can effectively solve the above two problems. Using active learning strategies in algorithm can obtain a large amount of in...

This work addresses the problem of segmenting an object of interest out of a video. We show that video object segmentation can be naturally cast as a semi-supervised learning problem and be efficiently solved using harmonic functions. We propose an incremental self-training approach by iteratively labeling the least uncertain frame and updating similarity metrics. Our self-training video segmen...

Semi-supervised word alignment aims to improve the accuracy of automatic word alignment by incorporating full or partial manual alignments. Motivated by standard active learning query sampling frameworks like uncertainty-, marginand query-by-committee sampling we propose multiple query strategies for the alignment link selection task. Our experiments show that by active selection of uncertain a...

Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling proce...

In many occasions in real life, we are faced with the problem of classification of partially labeled data, or semi-supervised learning. We consider the special case of scarcely labeled data or when the labeled data is insufficient, and present a principled method which implements active learning in scarcely labeled data to enhance the performance of the learner. This method is based on the rece...