Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Package details |
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Author | Han Cao [cre, aut, cph], Emanuel Schwarz [aut] |
Maintainer | Han Cao <hank9cao@gmail.com> |
License | GPL-3 |
Version | 0.9 |
URL | https://github.com/transbioZI/RMTL |
Package repository | View on GitHub |
Installation |
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