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RMTL  

Regularized Multi-Task Learning
View on CRAN: Click here


Download and install RMTL package within the R console
Install from CRAN:
install.packages("RMTL")

Install from Github:
library("remotes")
install_github("cran/RMTL")

Install by package version:
library("remotes")
install_version("RMTL", "0.9.9")



Attach the package and use:
library("RMTL")
Maintained by
Han Cao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-27
Latest Update: 2022-05-02
Description:
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) .
How to cite:
Han Cao (2019). RMTL: Regularized Multi-Task Learning. R package version 0.9.9, https://cran.r-project.org/web/packages/RMTL. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.9 (2019-02-27 18:00)
Other packages that cited RMTL R package
View RMTL citation profile
Other R packages that RMTL depends, imports, suggests or enhances
Complete documentation for RMTL
Functions, R codes and Examples using the RMTL R package
Some associated functions: Create_simulated_data . MTL . RMTL-package . calcError . cvMTL . plot.cvMTL . plotObj . predict.MTL . print.MTL . 
Some associated R codes: Cluster_penalty.R . Create_simulated_data.R . Graph_penalty.R . L1_norm.R . L21_norm.R . MTL.R . RMTL_package.R . Trace_norm.R . utiles.R .  Full RMTL package functions and examples
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