An R Library for Multi-task Learning
This package provides an efficient implementation of regularized multi-task learning comprising 10 algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. All algorithms are implemented basd on the accelerated gradient descent method and feature a complexity of O(1/k^2). Sparse model structure is induced by the solving the proximal operator.
#it will take a while
R CMD check ./
R CMD build ./
R CMD INSTALL RMTL_1.0.tar.gzPlease check "RMTL-manuel.pdf" for more details.
If you have any question, please contact: hank9cao@gmail.com