Open AccessProceedings Article
Multi-Instance Multi-Label Learning with Application to Scene Classification
Zhi-Hua Zhou,Min-Ling Zhang +1 more
- 04 Dec 2006
- Vol. 19, pp 1609-1616
TL;DR: This paper formalizes multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels, and proposes the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification.
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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, eg 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
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Citations
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
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Mining Multi-label Data
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- 01 Jan 2009
TL;DR: A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L, however, training examples in several application domains are often associated withA set of labels Y ⊆ L.
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Multi-instance Multi-label Learning for Relation Extraction
Mihai Surdeanu,Julie Tibshirani,Ramesh Nallapati,Christopher D. Manning +3 more
- 12 Jul 2012
TL;DR: This work proposes a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables that performs competitively on two difficult domains.
Multiple instance classification: Review, taxonomy and comparative study
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Simultaneous image classification and annotation
Chong Wang,David M. Blei,Fei-Fei Li +2 more
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TL;DR: A new probabilistic model for jointly modeling the image, its class label, and its annotations is developed, which derives an approximate inference and estimation algorithms based on variational methods, as well as efficient approximations for classifying and annotating new images.
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