active learning

Active Learning

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High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

"active Boosted Learning" Active Boosted Learning (actboost)

Active learning deals with the problem of selecting a small subset of examples to label, from a pool of unlabeled data, for training a good classifier. We develop an active learning algorithm in the boosting framework. In contrast to much of the recent efforts, which has focused on selecting the most ambiguous unlabeled example to label based on the current learned classifier, our algorithm sel...

Re-Active Learning: Active Learning with Relabeling

Active learning seeks to train the best classifier at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially...

Active Learning by Learning

Pool-based active learning is an important technique that helps reduce labeling efforts within a pool of unlabeled instances. Currently, most pool-based active learning strategies are constructed based on some human-designed philosophy; that is, they reflect what human beings assume to be “good labeling questions.” However, while such human-designed philosophies can be useful on specific data s...

Active Learning