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  "Title": "Non Metric Space (Approximate) Library",
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  "Authors@R": "c( person(\"Lampros\", \"Mouselimis\", email = \"mouselimislampros@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"https://orcid.org/0000-0002-8024-1546\")), person(\"B.\", \"Naidan\", role = \"cph\", comment = \"Author of the Non-Metric Space Library (NMSLIB)\"), person(\"L.\", \"Boytsov\", role = \"cph\", comment = \"Author of the Non-Metric Space Library (NMSLIB)\"), person(\"Yu.\", \"Malkov\", role = \"cph\", comment = \"Author of the Non-Metric Space Library (NMSLIB)\"), person(\"B.\", \"Frederickson\", role = \"cph\", comment = \"Author of the Non-Metric Space Library (NMSLIB)\"), person(\"D.\", \"Novak\", role = \"cph\", comment = \"Author of the Non-Metric Space Library (NMSLIB)\") )",
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  "Description": "A Non-Metric Space Library ('NMSLIB'\n<https://github.com/nmslib/nmslib>) wrapper, which according to\nthe authors \"is an efficient cross-platform similarity search\nlibrary and a toolkit for evaluation of similarity search\nmethods. The goal of the 'NMSLIB'\n<https://github.com/nmslib/nmslib> Library is to create an\neffective and comprehensive toolkit for searching in generic\nnon-metric spaces. Being comprehensive is important, because no\nsingle method is likely to be sufficient in all cases. Also\nnote that exact solutions are hardly efficient in high\ndimensions and/or non-metric spaces. Hence, the main focus is\non approximate methods\". The wrapper also includes Approximate\nKernel k-Nearest-Neighbor functions based on the 'NMSLIB'\n<https://github.com/nmslib/nmslib> 'Python' Library.",
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