Package: RMTL 0.9
RMTL: 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) <doi:10.1093/bioinformatics/bty831>.
Authors:
RMTL_0.9.tar.gz
RMTL_0.9.zip(r-4.5)RMTL_0.9.zip(r-4.4)RMTL_0.9.zip(r-4.3)
RMTL_0.9.tgz(r-4.4-any)RMTL_0.9.tgz(r-4.3-any)
RMTL_0.9.tar.gz(r-4.5-noble)RMTL_0.9.tar.gz(r-4.4-noble)
RMTL_0.9.tgz(r-4.4-emscripten)RMTL_0.9.tgz(r-4.3-emscripten)
RMTL.pdf |RMTL.html✨
RMTL/json (API)
# Install 'RMTL' in R: |
install.packages('RMTL', repos = c('https://transbiozi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/transbiozi/rmtl/issues
low-rank-representaionmulti-task-learningregularizationsparse-coding
Last updated 6 years agofrom:8e5bf4dcd4. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 12 2024 |
R-4.5-win | OK | Nov 12 2024 |
R-4.5-linux | OK | Nov 12 2024 |
R-4.4-win | OK | Nov 12 2024 |
R-4.4-mac | OK | Nov 12 2024 |
R-4.3-win | OK | Nov 12 2024 |
R-4.3-mac | OK | Nov 12 2024 |
Exports:calcErrorCreate_simulated_datacvMTLMTLplotObj
Dependencies:codetoolscorpcordoParallelforeachGPArotationiteratorslatticeMASSmnormtnlmepsych
Readme and manuals
Help Manual
Help page | Topics |
---|---|
RMTL: Regularized Multi-Task Learning | RMTL-package |
Calculate the prediction error | calcError |
Create an example dataset for testing the MTL algorithm | Create_simulated_data |
K-fold cross-validation | cvMTL |
Train a multi-task learning model. | MTL |
Plot the cross-validation curve | plot.cvMTL |
Plot the historical values of objective function | plotObj |
Predict the outcomes of new individuals | predict.MTL |
Print the meta information of the model | print.MTL |