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  "Date": "2023-01-28",
  "Title": "A Generalized Multiclass Support Vector Machine",
  "Authors@R": "c(person(\"Gertjan\", \"van den Burg\", role=c(\"aut\", \"cre\"),\nemail=\"gertjanvandenburg@gmail.com\"),\nperson(\"Patrick\", \"Groenen\", email=\"groenen@ese.eur.nl\", role=\"ctb\"))",
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  "Description": "The GenSVM classifier is a generalized multiclass support\nvector machine (SVM). This classifier aims to find decision\nboundaries that separate the classes with as wide a margin as\npossible. In GenSVM, the loss function is very flexible in the\nway that misclassifications are penalized.  This allows the\nuser to tune the classifier to the dataset at hand and\npotentially obtain higher classification accuracy than\nalternative multiclass SVMs.  Moreover, this flexibility means\nthat GenSVM has a number of other multiclass SVMs as special\ncases. One of the other advantages of GenSVM is that it is\ntrained in the primal space, allowing the use of warm starts\nduring optimization.  This means that for common tasks such as\ncross validation or repeated model fitting, GenSVM can be\ntrained very quickly. Based on: G.J.J. van den Burg and P.J.F.\nGroenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.",
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