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  "Title": "Density Convoluted Support Vector Machines",
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  "Date": "2025-01-08",
  "Authors@R": "c(\nperson(given = \"Boxiang\",\nfamily = \"Wang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"boxiang-wang@uiowa.edu\"),\nperson(given = \"Le\",\nfamily = \"Zhou\",\nrole = \"aut\"),\nperson(given = \"Yuwen\",\nfamily = \"Gu\",\nrole = \"aut\"),\nperson(given = \"Hui\",\nfamily = \"Zou\",\nrole = \"aut\"))",
  "Description": "Implements an efficient algorithm for solving\nsparse-penalized support vector machines with kernel density\nconvolution. This package is designed for high-dimensional\nclassification tasks, supporting lasso (L1) and elastic-net\npenalties for sparse feature selection and providing options\nfor tuning kernel bandwidth and penalty weights. The 'dcsvm' is\napplicable to fields such as bioinformatics, image analysis,\nand text classification, where high-dimensional data commonly\narise. Learn more about the methodology and algorithm at Wang,\nZhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.",
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