The MALSAR (Multi-tAsk Learning via StructurAl Regularization) package includes the following multi-task learning algorithms: Mean-Regularized Multi-Task Learning; Multi-Task Learning with Joint Feature Selection; Robust Multi-Task Feature Learning; Trace-Norm Regularized Multi-Task Learning; Alternating Structural Optimization; Incoherent Low-Rank and Sparse Learning; Robust Low-Rank Multi-Task Learning; Clustered Multi-Task Learning; Multi-Task Learning with Graph Structures; Disease Progression Models; Incomplete Multi-Source Fusion (iMSF); Multi-Stage Multi-Source Fusion; Multi-Task Clustering.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Jeong, Jun-Yong; Kang, Ju-Seok; Jun, Chi-Hyuck: Regularization-based model tree for multi-output regression (2020)
- Fan, Ya-Ru; Wang, Yilun; Huang, Ting-Zhu: Enhanced joint sparsity via iterative support detection (2017)
- Goncalves, André R.; von Zuben, Fernando J.; Banerjee, Arindam: Multi-task sparse structure learning with Gaussian copula models (2016)
- Spyromitros-Xioufis, Eleftherios; Tsoumakas, Grigorios; Groves, William; Vlahavas, Ioannis: Multi-target regression via input space expansion: treating targets as inputs (2016)
- Liu, Han; Wang, Lie; Zhao, Tuo: Calibrated multivariate regression with application to neural semantic basis discovery (2015)