Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Learning the optimal neighborhood kernel for classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Structured max-margin learning for multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Pattern Recognition Letters
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In this paper, a novel method is developed for enabling Multi-Kernel Multi-Label Learning. Interlabel dependency and similarity diversity are simultaneously leveraged in the proposed method. A concept network is constructed to capture the inter-label correlations for classifier training. Maximal margin approach is used to effectively formulate the feature-label associations and the labellabel correlations. Specific kernels are learned not only for each label but also for each pair of the inter-related labels. By learning the eigenfunctions of the kernels, the similarity between a new data point and the training samples can be computed in the online mode. Our experimental results on real datasets (web pages, images, music, and bioinformatics) have demonstrated the effectiveness of our method.