BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Drosophila gene expression pattern annotation using sparse features and term-term interactions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Ensemble multi-instance multi-label learning approach for video annotation task
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
Multi-kernel multi-label learning with max-margin concept network
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework, Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.