- Label Distribution Learning
- Description: Matlab Codes and Data Sets for Label Distribution Learning.
Reference: X. Geng, C. Yin, and Z.-H. Zhou. Facial Age Estimation by Learning from Label Distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2013, 35(10): 2401-2412.
- Multi-label classifier with label-specific features
- Description: This toolbox contains programs for the multi-label classifier which utilizes label-specific features.
Reference: M.-L. Zhang, L. Wu. LIFT: Multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.
Requirement: The matlab package for Libsvm should be used in conjunction with this toolbox.
- Partial label learning without disambiguation
- Description: The package includes the MATLAB code of PL_ECOC, which is designed for learning from partial label data by adapting the ECOC techniques. A Readme file and some sample files are included in the package.
Reference:M.-L. Zhang. Disambiguation-free partial label learning. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM'14), Philadelphia, PA, 2014, in press.
- Inductive semi-supervised multi-label learning
- Description: The package includes the MATLAB code of iMLCU, which is designed for learning from labeled and unlabeled multi-label data under inductive setting. A Readme file and some sample files are included in the package.
ReferenceL. Wu, M.-L. Zhang. Multi-label classification with unlabeled data: An inductive approach. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, 197-212.