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...
Identifying suitable image features is a central challenge in computer vision, ranging from representations for lowlevel to high-level vision. Due to the difficulty of this task,...
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
The ability to interpret demonstrations from the perspective of the teacher plays a critical role in human learning. Robotic systems that aim to learn effectively from human teach...
Matt Berlin, Jesse Gray, Andrea Lockerd Thomaz, Cy...
Learning tasks from a single demonstration presents a significant challenge because the observed sequence is inherently an incomplete representation of the procedure that is speci...
Hyuckchul Jung, James F. Allen, Nathanael Chambers...