Regularized Multi-Task Learning

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) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("RMTL")

0.9 by Han Cao, 3 months ago


https://github.com/transbioZI/RMTL


Report a bug at https://github.com/transbioZI/RMTL/issues


Browse source code at https://github.com/cran/RMTL


Authors: Han Cao [cre, aut, cph] , Emanuel Schwarz [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports MASS, psych, corpcor, doParallel, foreach

Suggests knitr, rmarkdown


See at CRAN