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  "Title": "Regularized Multi-Task Learning",
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  "Description": "Efficient solvers for 10 regularized multi-task learning\nalgorithms applicable for regression, classification, joint\nfeature selection, task clustering, low-rank learning, sparse\nlearning and network incorporation. Based on the accelerated\ngradient descent method, the algorithms feature a state-of-art\ncomputational complexity O(1/k^2). Sparse model structure is\ninduced by the solving the proximal operator. The detail of the\npackage is described in the paper of Han Cao and Emanuel\nSchwarz (2018) <doi:10.1093/bioinformatics/bty831>.",
  "URL": "https://github.com/transbioZI/RMTL",
  "BugReports": "https://github.com/transbioZI/RMTL/issues",
  "Date": "2026-02-20",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "Repository": "https://transbiozi.r-universe.dev",
  "Date/Publication": "2026-02-20 12:57:27 UTC",
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      "title": "Calculate the prediction error",
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      "topics": [
        "MTL"
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    {
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      "title": "Predict the outcomes of new individuals",
      "topics": [
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      "title": "An Tutorial for Regularized Multi-task Learning using the package RMTL",
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