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, multi-instance learning and multi-label learning. Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification.
CITATION STYLE
Zhou, Z. H., & Zhang, M. L. (2006). Multi-Instance Multi-Label Learning with Application to Scene Classification. In NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems (pp. 1609–1616). MIT Press Journals. https://doi.org/10.7551/mitpress/7503.003.0206
Mendeley helps you to discover research relevant for your work.