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) .


Reference manual

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0.9 by Han Cao, a year ago

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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