nips nips2006 nips2006-136 nips2006-136-reference knowledge-graph by maker-knowledge-mining
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Author: Zhi-hua Zhou, Min-ling Zhang
Abstract: In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e.g. an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways. We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multiinstance learning and multi-label learning. Then, we propose the M IML B OOST and M IML S VM algorithms which achieve good performance in an application to scene classification. 1
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