Active learning is a subfield of machine learning that aims to reduce the number of labeled information while receiving the same classification performance. As it is easy to capture unlabeled information from sensors but expensive to annotate this data, the influence of active learning increases fast. Personalized search engines, for instance, need user feedbacks to […]
Category: Publications
Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency
by Georg Krempl, Daniel Kottke Mining data with minimal annotation costs requires efficient active approaches, that ideally select the optimal candidate for labelling under a user-specified classification performance measure. Common generic approaches, that are usable with any classifier and any performance measure, are either slow like error reduction, or heuristics like uncertainty sampling. In contrast, […]
Probabilistic Active Learning: A Short Proposition
by Georg Krempl, Daniel Kottke, Myra Spiliopoulou. Active Mining of Big Data requires fast approaches that ideally select for a user-specified performance measure and arbitrary classifier the optimal instance for improving the classification performance. Existing generic approaches are either slow, like error reduction, or heuristics, like uncertainty sampling. We propose a novel, fast yet versatile […]
Interactive Object Detection in Movies (Bachelor’s Thesis)
The importance of video annotation is constantly rising, especially in areas of multimedia retrieval, semantic search and even in newest research in neuroscience. The list of pixel-accurate, hierarchical and automatic object detection tools is still empty, due to the excessive complexity. The approach is to roll out the automation to a problem specific detection algorithm. […]