The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Bioinformatics
Flexible latent variable models for multi-task learning
Machine Learning
Efficient Visual Search of Videos Cast as Text Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Object type recognition for automated analysis of protein subcellular location
IEEE Transactions on Image Processing
Learning frame relevance for video classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semi-supervised multi-instance multi-label learning for video annotation task
Proceedings of the 20th ACM international conference on Multimedia
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.