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...
In this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of photographs and assign names to these phot...
Lei Zhang, Yuxiao Hu, Mingjing Li, Wei-Ying Ma, Ho...
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
We propose and study a new ranking problem in versioned databases. Consider a database of versioned objects which have different valid instances along a history (e.g., documents i...
Leong Hou U, Nikos Mamoulis, Klaus Berberich, Srik...
The Replica Placement Problem (RPP) aims at creating a set of duplicated data objects across the nodes of a distributed system in order to optimize certain criteria. Typically, RP...
Thanasis Loukopoulos, Petros Lampsas, Ishfaq Ahmad