Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Convex Optimization
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning from ambiguously labeled examples
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A New SVM Approach to Multi-instance Multi-label Learning
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
The Journal of Machine Learning Research
Multi-instance multi-label learning
Artificial Intelligence
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Multi-modal image annotation with multi-instance multi-label LDA
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Constrained instance clustering in multi-instance multi-label learning
Pattern Recognition Letters
Hi-index | 0.00 |
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior work on MIML has focused on predicting label sets for previously unseen bags. We instead consider the problem of predicting instance labels while learning from data labeled only at the bag level. We propose Rank-Loss Support Instance Machines, which optimize a regularized rank-loss objective and can be instantiated with different aggregation models connecting instance-level predictions with bag-level predictions. The aggregation models that we consider are equivalent to defining a "support instance" for each bag, which allows efficient optimization of the rank-loss objective using primal sub-gradient descent. Experiments on artificial and real-world datasets show that the proposed methods achieve higher accuracy than other loss functions used in prior work, e.g., Hamming loss, and recent work in ambiguous label classification.