Inside the Selection Box: Visualising active learning selection strategies

Active Learning Query Selection with Historical Information

This work describes novel methods and techniques to decrease the cost of employing active learning in text categorisation problems. The cost of performing active learning is a combination of labelling effort and computational overhead. Reducing the cost of active learning allows for accurate classifiers to be constructed inexpensively, increasing the number of realworld problems where machine l...

Active Learning Selection Strategies for Information Extraction

The need for labeled documents is a key bottleneck in adaptive information extraction. One way to solve this problem is through active learning algorithms that require users to label only the most informative documents. We investigate several document selection strategies that are particularly relevant to information extraction. We show that some strategies are biased toward recall, while other...

Real-time functional MRI Classication of Brain States using Markov-SVM Hybrid Models: Peering inside the rt-fMRI black box

Machine Learning (ML) methods applied to real-time functional MRI (rt-fMRI) data provide the ability to predict and detect online any changes in cognitive states. Applications based on rt-fMRI require appropriate selection of features, preprocessing routines, and models in order to both be practical to implement and deliver interpretable results. In the paper, we evaluate blind MVPA feature sel...

The Biographical Inventory in Naval Aviation Selection: Inside the Black Box The Biographical Inventory in Naval Aviation Selection: Inside the Black Box

Active Learning for Mention Detection: A Comparison of Sentence Selection Strategies

We propose and compare various sentence selection strategies for active learning for the task of detecting mentions of entities. The best strategy employs the sum of con dences of two statistical classi ers trained on di erent views of the data. Our experimental results show that, compared to the random selection strategy, this strategy reduces the amount of required labeled training data by ov...