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.
|Author||Han Cao, Emanuel Schwarz|
|Maintainer||Han Cao <[email protected]>|
|Package repository||View on GitHub|
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