transbioZI/RMTL: RMTL: 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.

Getting started

Package details

AuthorHan Cao, Emanuel Schwarz
MaintainerHan Cao <[email protected]>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
transbioZI/RMTL documentation built on Dec. 21, 2018, 2:04 p.m.