Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
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Rong Jin (Michigan State University), Shijun Wang...
While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition tha...
Ce Liu, Jenny Yuen, Antonio B. Torralba, Josef Siv...
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Web user search customization research has been fueled by the recognition that if the WWW is to attain to its optimal potential as an interactive medium the development of new and...
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...