Multiple-instance learning with pairwise instance similarity - Yuan, Liming; Liu, Jiafeng; Tang, Xianglong (Zielona Góra: Uniwersytet Zielonogórski)
Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space.
Keywords: multiple-instance learning; instance selection; similarity; support vector machines