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info.json
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{
"abstract": "In this paper, we present an open source Error-Correcting Output\nCodes (ECOC) library. The ECOC framework is a powerful tool to\ndeal with multi-class categorization problems. This library\ncontains both state-of-the-art coding (one-versus-one,\none-versus-all, dense random, sparse random, DECOC, forest-ECOC,\nand ECOC-ONE) and decoding designs (hamming, euclidean, inverse\nhamming, laplacian, β-density, attenuated, loss-based,\nprobabilistic kernel-based, and loss-weighted) with the parameters\ndefined by the authors, as well as the option to include your own\ncoding, decoding, and base classifier.",
"authors": [
"Sergio Escalera",
"Oriol Pujol",
"Petia Radeva"
],
"id": "escalera10a",
"issue": 20,
"pages": [
661,
664
],
"title": "Error-Correcting Output Codes Library",
"volume": "11",
"year": "2010",
"special_issue": "MLOSS",
"extra_links": [["code", "Code.zip"]]
}