. . . . . "\u0417\u0430\u0434\u0430\u0447\u0430 \u043F\u043E\u0438\u0441\u043A\u0430 \u0431\u043B\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043E \u0441\u043E\u0441\u0435\u0434\u0430 \u0437\u0430\u043A\u043B\u044E\u0447\u0430\u0435\u0442\u0441\u044F \u0432 \u043E\u0442\u044B\u0441\u043A\u0430\u043D\u0438\u0438 \u0441\u0440\u0435\u0434\u0438 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432\u0430 \u044D\u043B\u0435\u043C\u0435\u043D\u0442\u043E\u0432, \u0440\u0430\u0441\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u043D\u044B\u0445 \u0432 \u043C\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043A\u043E\u043C \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u0435, \u044D\u043B\u0435\u043C\u0435\u043D\u0442\u043E\u0432 \u0431\u043B\u0438\u0437\u043A\u0438\u0445 \u043A \u0437\u0430\u0434\u0430\u043D\u043D\u043E\u043C\u0443, \u0441\u043E\u0433\u043B\u0430\u0441\u043D\u043E \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0439 \u0437\u0430\u0434\u0430\u043D\u043D\u043E\u0439 \u0444\u0443\u043D\u043A\u0446\u0438\u0438 \u0431\u043B\u0438\u0437\u043E\u0441\u0442\u0438, \u043E\u043F\u0440\u0435\u0434\u0435\u043B\u044F\u044E\u0449\u0435\u0439 \u044D\u0442\u043E \u043C\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043A\u043E\u0435 \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u043E."@ru . . . . . . "\u0417\u0430\u0434\u0430\u0447\u0430 \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u0441\u0443\u0441\u0456\u0434\u0430 \u0454 \u0437\u0430\u0434\u0430\u0447\u0435\u044E \u043E\u043F\u0442\u0438\u043C\u0456\u0437\u0430\u0446\u0456\u0457, \u044F\u043A\u0430 \u043F\u043E\u043B\u044F\u0433\u0430\u0454 \u0443 \u0432\u0456\u0434\u0448\u0443\u043A\u0430\u043D\u043D\u0456 \u0443 \u043C\u043D\u043E\u0436\u0438\u043D\u0456 \u0435\u043B\u0435\u043C\u0435\u043D\u0442\u0456\u0432, \u0440\u043E\u0437\u0442\u0430\u0448\u043E\u0432\u0430\u043D\u0438\u0445 \u0443 \u0431\u0430\u0433\u0430\u0442\u043E\u0432\u0438\u043C\u0456\u0440\u043D\u043E\u043C\u0443 \u043C\u0435\u0442\u0440\u0438\u0447\u043D\u043E\u043C\u0443 \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u0456, \u0435\u043B\u0435\u043C\u0435\u043D\u0442\u0456\u0432, \u0431\u043B\u0438\u0437\u044C\u043A\u0438\u0445 \u0434\u043E \u0437\u0430\u0434\u0430\u043D\u043E\u0433\u043E, \u0432\u0456\u0434\u043F\u043E\u0432\u0456\u0434\u043D\u043E \u0434\u043E \u0437\u0430\u0434\u0430\u043D\u043E\u0457 \u0444\u0443\u043D\u043A\u0446\u0456\u0457 \u0431\u043B\u0438\u0437\u044C\u043A\u043E\u0441\u0442\u0456. \u0424\u043E\u0440\u043C\u0430\u043B\u044C\u043D\u043E \u0446\u044F \u0437\u0430\u0434\u0430\u0447\u0430 \u0441\u0442\u0430\u0432\u0438\u0442\u044C\u0441\u044F \u043D\u0430\u0441\u0442\u0443\u043F\u043D\u0438\u043C \u0447\u0438\u043D\u043E\u043C: \u043D\u0430\u0434\u0430\u043D\u043E \u043C\u043D\u043E\u0436\u0438\u043D\u0443 \u0442\u043E\u0447\u043E\u043A S \u0443 \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u0456 M \u0442\u0430 \u0442\u043E\u0447\u043A\u0443 q \u2208 M, \u043D\u0435\u043E\u0431\u0445\u0456\u0434\u043D\u043E \u0437\u043D\u0430\u0439\u0442\u0438 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u0443 \u0434\u043E q \u0442\u043E\u0447\u043A\u0443 \u0432 S. \u0414\u043E\u043D\u0430\u043B\u044C\u0434 \u041A\u043D\u0443\u0442 \u0432 \u041C\u0438\u0441\u0442\u0435\u0446\u0442\u0432\u0456 \u043F\u0440\u043E\u0433\u0440\u0430\u043C\u0443\u0432\u0430\u043D\u043D\u044F (\u0442\u043E\u043C 3, 1973) \u043D\u0430\u0437\u0432\u0430\u0432 \u0446\u0435 \u043F\u0440\u043E\u0431\u043B\u0435\u043C\u043E\u044E \u043F\u043E\u0448\u0442\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0434\u0456\u043B\u0435\u043D\u043D\u044F, \u043F\u043E\u0441\u0438\u043B\u0430\u044E\u0447\u0438\u0441\u044C \u043D\u0430 \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F \u0446\u0456\u0454\u0457 \u0437\u0430\u0434\u0430\u0447\u0456 \u0434\u043E \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u043F\u043E\u0448\u0442\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0434\u0456\u043B\u0435\u043D\u043D\u044F. \u041F\u0440\u044F\u043C\u0438\u043C \u0443\u0437\u0430\u0433\u0430\u043B\u044C\u043D\u0435\u043D\u043D\u044F\u043C \u0437\u0430\u0434\u0430\u0447\u0456 \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u0441\u0443\u0441\u0456\u0434\u0430 \u0454 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0448\u0443\u043A\u0443 k-NN, \u044F\u043A\u0438\u0439 \u043F\u0440\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u0438\u0439 \u0434\u043B\u044F \u043F\u043E\u0448\u0443\u043A\u0443 k \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u0438\u0445 \u0442\u043E\u0447\u043E\u043A. \u041D\u0430\u0439\u0447\u0430\u0441\u0442\u0456\u0448\u0435 M \u0454 \u043C\u0435\u0442\u0440\u0438\u0447\u043D\u0438\u043C \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u043E\u043C \u0456 \u0437\u0430\u043F\u0440\u043E\u0432\u0430\u0434\u0436\u0443\u0454\u0442\u044C\u0441\u044F \u0444\u0443\u043D\u043A\u0446\u0456\u044F \u0431\u043B\u0438\u0437\u044C\u043A\u043E\u0441\u0442\u0456, \u0449\u043E \u0432\u0438\u0437\u043D\u0430\u0447\u0430\u0454\u0442\u044C\u0441\u044F \u044F\u043A \u043C\u0435\u0442\u0440\u0438\u043A\u0430, \u044F\u043A\u0430 \u0454 \u0441\u0438\u043C\u0435\u0442\u0440\u0438\u0447\u043D\u043E\u044E \u0456 \u0437\u0430\u0434\u043E\u0432\u043E\u043B\u044C\u043D\u044F\u0454 \u043D\u0435\u0440\u0456\u0432\u043D\u043E\u0441\u0442\u0456 \u0442\u0440\u0438\u043A\u0443\u0442\u043D\u0438\u043A\u0430. \u0429\u0435 \u0437\u0430\u0433\u0430\u043B\u044C\u043D\u0456\u0448\u0435, M \u2014 \u0446\u0435 d-\u0432\u0438\u043C\u0456\u0440\u043D\u0438\u0439 \u0432\u0435\u043A\u0442\u043E\u0440\u043D\u0438\u0439 \u043F\u0440\u043E\u0441\u0442\u0456\u0440, \u0432 \u044F\u043A\u043E\u043C\u0443 \u0431\u043B\u0438\u0437\u044C\u043A\u0456\u0441\u0442\u044C \u0431\u0435\u0440\u0435\u0442\u044C\u0441\u044F \u044F\u043A \u0415\u0432\u043A\u043B\u0456\u0434\u043E\u0432\u0430 \u043C\u0435\u0442\u0440\u0438\u043A\u0430, \u0432\u0443\u043B\u0438\u0447\u043D\u0430 \u043C\u0435\u0442\u0440\u0438\u043A\u0430 \u0430\u0431\u043E \u0456\u043D\u0448\u0456 \u043C\u0435\u0442\u0440\u0438\u043A\u0438. \u041E\u0434\u043D\u0430\u043A \u0444\u0443\u043D\u043A\u0446\u0456\u044F \u0431\u043B\u0438\u0437\u044C\u043A\u043E\u0441\u0442\u0456 \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u0434\u043E\u0432\u0456\u043B\u044C\u043D\u043E\u044E. \u041E\u0434\u043D\u0438\u043C \u0437 \u043F\u0440\u0438\u043A\u043B\u0430\u0434\u0456\u0432 \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 , \u0434\u043B\u044F \u044F\u043A\u043E\u0457 \u043D\u0435\u0440\u0456\u0432\u043D\u0456\u0441\u0442\u044C \u0442\u0440\u0438\u043A\u0443\u0442\u043D\u0438\u043A\u0430 \u043D\u0435 \u0432\u0438\u043A\u043E\u043D\u0443\u0454\u0442\u044C\u0441\u044F."@uk . . . "\u0417\u0430\u0434\u0430\u0447\u0430 \u043F\u043E\u0438\u0441\u043A\u0430 \u0431\u043B\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043E \u0441\u043E\u0441\u0435\u0434\u0430 \u0437\u0430\u043A\u043B\u044E\u0447\u0430\u0435\u0442\u0441\u044F \u0432 \u043E\u0442\u044B\u0441\u043A\u0430\u043D\u0438\u0438 \u0441\u0440\u0435\u0434\u0438 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432\u0430 \u044D\u043B\u0435\u043C\u0435\u043D\u0442\u043E\u0432, \u0440\u0430\u0441\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u043D\u044B\u0445 \u0432 \u043C\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043A\u043E\u043C \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u0435, \u044D\u043B\u0435\u043C\u0435\u043D\u0442\u043E\u0432 \u0431\u043B\u0438\u0437\u043A\u0438\u0445 \u043A \u0437\u0430\u0434\u0430\u043D\u043D\u043E\u043C\u0443, \u0441\u043E\u0433\u043B\u0430\u0441\u043D\u043E \u043D\u0435\u043A\u043E\u0442\u043E\u0440\u043E\u0439 \u0437\u0430\u0434\u0430\u043D\u043D\u043E\u0439 \u0444\u0443\u043D\u043A\u0446\u0438\u0438 \u0431\u043B\u0438\u0437\u043E\u0441\u0442\u0438, \u043E\u043F\u0440\u0435\u0434\u0435\u043B\u044F\u044E\u0449\u0435\u0439 \u044D\u0442\u043E \u043C\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043A\u043E\u0435 \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u043E."@ru . . . "\u0628\u062D\u062B \u0627\u0644\u0646\u0642\u0637\u0629 \u0627\u0644\u0623\u0642\u0631\u0628"@ar . "7309022"^^ . . "\u6700\u8FD1\u508D\u63A2\u7D22\uFF08\u82F1: Nearest neighbor search, NNS\uFF09\u306F\u3001\u8DDD\u96E2\u7A7A\u9593\u306B\u304A\u3051\u308B\u6700\u3082\u8FD1\u3044\u70B9\u3092\u63A2\u3059\u6700\u9069\u5316\u554F\u984C\u306E\u4E00\u7A2E\u3001\u3042\u308B\u3044\u306F\u305D\u306E\u89E3\u6CD5\u3002\u8FD1\u63A5\u63A2\u7D22\uFF08\u82F1: proximity search\uFF09\u3001\u985E\u4F3C\u63A2\u7D22\uFF08\u82F1: similarity search\uFF09\u3001\u6700\u8FD1\u70B9\u63A2\u7D22\uFF08\u82F1: closest point search\uFF09\u306A\u3069\u3068\u3082\u547C\u3076\u3002\u554F\u984C\u306F\u3059\u306A\u308F\u3061\u3001\u8DDD\u96E2\u7A7A\u9593 M \u306B\u304A\u3051\u308B\u70B9\u306E\u96C6\u5408 S \u304C\u3042\u308A\u3001\u30AF\u30A8\u30EA\u70B9 q \u2208 M \u304C\u3042\u308B\u3068\u304D\u3001S \u306E\u4E2D\u3067 q \u306B\u6700\u3082\u8FD1\u3044\u70B9\u3092\u63A2\u3059\u3001\u3068\u3044\u3046\u554F\u984C\u3067\u3042\u308B\u3002\u591A\u304F\u306E\u5834\u5408\u3001M \u306B\u306F d\u6B21\u5143\u306E\u30E6\u30FC\u30AF\u30EA\u30C3\u30C9\u7A7A\u9593\u304C\u63A1\u7528\u3055\u308C\u3001\u8DDD\u96E2\u306F\u30E6\u30FC\u30AF\u30EA\u30C3\u30C9\u8DDD\u96E2\u304B\u30DE\u30F3\u30CF\u30C3\u30BF\u30F3\u8DDD\u96E2\u3067\u6E2C\u5B9A\u3055\u308C\u308B\u3002\u4F4E\u6B21\u5143\u306E\u5834\u5408\u3068\u9AD8\u6B21\u5143\u306E\u5834\u5408\u3067\u7570\u306A\u308B\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u304C\u3068\u3089\u308C\u308B\u3002 \u30C9\u30CA\u30EB\u30C9\u30FB\u30AF\u30CC\u30FC\u30B9\u306F\u3001The Art of Computer Programming Vol.3\uFF081973\u5E74\uFF09\u3067\u3001\u3053\u308C\u3092\u90F5\u4FBF\u5C40\u306E\u554F\u984C\u3067\u8868\u3057\u305F\u3002\u3053\u308C\u306F\u3059\u306A\u308F\u3061\u3001\u3042\u308B\u4F4F\u6240\u306B\u6700\u3082\u8FD1\u3044\u90F5\u4FBF\u5C40\u3092\u6C42\u3081\u308B\u554F\u984C\u3067\u3042\u308B\u3002"@ja . . . . . . "\u6700\u90BB\u8FD1\u641C\u7D22\uFF08Nearest Neighbor Search, NNS\uFF09\u53C8\u79F0\u4E3A\u201C\u6700\u8FD1\u70B9\u641C\u7D22\u201D\uFF08Closest point search\uFF09\uFF0C\u662F\u4E00\u4E2A\u5728\u4E2D\u5BFB\u627E\u6700\u8FD1\u70B9\u7684\u4F18\u5316\u95EE\u9898\u3002\u95EE\u9898\u63CF\u8FF0\u5982\u4E0B\uFF1A\u5728\u5C3A\u5EA6\u7A7A\u95F4M\u4E2D\u7ED9\u5B9A\u4E00\u4E2A\u70B9\u96C6S\u548C\u4E00\u4E2A\u76EE\u6807\u70B9q \u2208 M\uFF0C\u5728S\u4E2D\u627E\u5230\u8DDD\u79BBq\u6700\u8FD1\u7684\u70B9\u3002\u5F88\u591A\u60C5\u51B5\u4E0B\uFF0CM\u4E3A\u591A\u7EF4\u7684\u6B27\u51E0\u91CC\u5F97\u7A7A\u95F4\uFF0C\u8DDD\u79BB\u7531\u6B27\u51E0\u91CC\u5F97\u8DDD\u79BB\u6216\u66FC\u54C8\u987F\u8DDD\u79BB\u51B3\u5B9A\u3002 \u9AD8\u5FB7\u7EB3\u5728\u300A\u8BA1\u7B97\u673A\u7A0B\u5E8F\u8BBE\u8BA1\u827A\u672F\u300B\uFF081973\uFF09\u4E00\u4E66\u7684\u7B2C\u4E09\u7AE0\u4E2D\u79F0\u4E4B\u4E3A\u90AE\u5C40\u95EE\u9898\uFF0C\u5373\u5C45\u6C11\u5BFB\u627E\u79BB\u81EA\u5DF1\u5BB6\u6700\u8FD1\u7684\u90AE\u5C40\u3002"@zh . . . . . . . . . . . . . . . . . "Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values."@en . . . "\u6700\u90BB\u8FD1\u641C\u7D22\uFF08Nearest Neighbor Search, NNS\uFF09\u53C8\u79F0\u4E3A\u201C\u6700\u8FD1\u70B9\u641C\u7D22\u201D\uFF08Closest point search\uFF09\uFF0C\u662F\u4E00\u4E2A\u5728\u4E2D\u5BFB\u627E\u6700\u8FD1\u70B9\u7684\u4F18\u5316\u95EE\u9898\u3002\u95EE\u9898\u63CF\u8FF0\u5982\u4E0B\uFF1A\u5728\u5C3A\u5EA6\u7A7A\u95F4M\u4E2D\u7ED9\u5B9A\u4E00\u4E2A\u70B9\u96C6S\u548C\u4E00\u4E2A\u76EE\u6807\u70B9q \u2208 M\uFF0C\u5728S\u4E2D\u627E\u5230\u8DDD\u79BBq\u6700\u8FD1\u7684\u70B9\u3002\u5F88\u591A\u60C5\u51B5\u4E0B\uFF0CM\u4E3A\u591A\u7EF4\u7684\u6B27\u51E0\u91CC\u5F97\u7A7A\u95F4\uFF0C\u8DDD\u79BB\u7531\u6B27\u51E0\u91CC\u5F97\u8DDD\u79BB\u6216\u66FC\u54C8\u987F\u8DDD\u79BB\u51B3\u5B9A\u3002 \u9AD8\u5FB7\u7EB3\u5728\u300A\u8BA1\u7B97\u673A\u7A0B\u5E8F\u8BBE\u8BA1\u827A\u672F\u300B\uFF081973\uFF09\u4E00\u4E66\u7684\u7B2C\u4E09\u7AE0\u4E2D\u79F0\u4E4B\u4E3A\u90AE\u5C40\u95EE\u9898\uFF0C\u5373\u5C45\u6C11\u5BFB\u627E\u79BB\u81EA\u5DF1\u5BB6\u6700\u8FD1\u7684\u90AE\u5C40\u3002"@zh . . "Recherche des plus proches voisins"@fr . . "La recherche des plus proches voisins, ou des k plus proches voisins, est un probl\u00E8me algorithmique classique. De fa\u00E7on informelle le probl\u00E8me consiste, \u00E9tant donn\u00E9 un point \u00E0 trouver, dans un ensemble d'autres points, quels sont les k plus proches."@fr . . "\uCD5C\uADFC\uC811 \uC774\uC6C3 \uD0D0\uC0C9(\uC601\uC5B4: nearest neighbor search)\uC740 \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 (\uB610\uB294 \uAC00\uC7A5 \uADFC\uC811\uD55C) \uC810\uC744 \uCC3E\uAE30 \uC704\uD55C \uCD5C\uC801\uD654 \uBB38\uC81C\uC774\uB2E4. \uADFC\uC811 \uD0D0\uC0C9(proximity search), \uC720\uC0AC\uB3C4 \uD0D0\uC0C9(similarity search), \uCD5C\uADFC\uC811 \uC810\uC30D \uBB38\uC81C(closest point search)\uB77C\uACE0\uB3C4 \uBD88\uB9B0\uB2E4. \uADFC\uC0AC(\u8FD1\u4F3C)\uB77C\uB294 \uAC1C\uB150\uC740 \uBCF4\uD3B8\uC801\uC73C\uB85C \uBB3C\uCCB4\uC640 \uBB3C\uCCB4\uAC00 \uB35C \uC720\uC0AC\uD560\uC218\uB85D \uADF8 \uD568\uC218\uC758 \uAC12\uC740 \uCEE4\uC9C0\uB294 \uC0C1\uC774(\u76F8\u7570) \uD568\uC218\uC5D0 \uC758\uD574\uC11C \uD45C\uD604\uB41C\uB2E4. \uC5C4\uBC00\uD558\uAC8C, \uCD5C\uADFC\uC811 \uC774\uC6C3 \uD0D0\uC0C9 \uBB38\uC81C\uB294 \uB2E4\uC74C\uACFC \uAC19\uC774 \uC815\uC758\uB41C\uB2E4: \uACF5\uAC04 M\uC5D0\uC11C\uC758 \uC810\uB4E4\uB85C \uC774\uB8E8\uC5B4\uC9C4 \uC9D1\uD569 S \uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C, \uCFFC\uB9AC\uC810 q \u2208 M\uC5D0 \uB300\uD574 S \uC548\uC5D0\uC11C \uAC00\uC7A5 q\uC640 \uAC00\uAE4C\uC6B4 \uC810\uC744 \uCC3E\uB294\uB2E4. \uB3C4\uB110\uB4DC \uCEE4\uB204\uC2A4\uB294 \uADF8\uC758 \uC800\uC11C \u300A\uCEF4\uD4E8\uD130 \uD504\uB85C\uADF8\uB798\uBC0D\uC758 \uC608\uC220\u300B(1973) \uC81C 3\uAD8C\uC5D0\uC11C \uC0AC\uB78C\uB4E4\uC758 \uAC70\uC8FC\uC9C0\uB97C \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 \uC6B0\uCCB4\uAD6D\uC5D0 \uBC30\uC815\uD558\uB294 \uD504\uB85C\uADF8\uB7A8\uC774\uB77C\uB294 \uC758\uBBF8\uC5D0\uC11C \uC774\uB97C \uC6B0\uCCB4\uAD6D \uBB38\uC81C\uB77C\uACE0 \uBA85\uBA85\uD588\uB2E4. \uC774 \uBB38\uC81C\uC758 \uC9C1\uC811\uC801\uC778 \uC77C\uBC18\uD654 \uBB38\uC81C\uB85C\uC368\uB294, k \uAC1C\uC758 \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 \uC810\uC744 \uCC3E\uB294 K-\uCD5C\uADFC\uC811 \uC774\uC6C3 \uC54C\uACE0\uB9AC\uC998\uC774 \uC788\uB2E4."@ko . . . . . . . . . "\u6700\u90BB\u8FD1\u641C\u7D22"@zh . . . . . . "25982"^^ . . . . . "\u0417\u0430\u0434\u0430\u0447\u0430 \u043F\u043E\u0438\u0441\u043A\u0430 \u0431\u043B\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043E \u0441\u043E\u0441\u0435\u0434\u0430"@ru . . . . . . . . . . . . . . "\u0645\u0633\u0623\u0644\u0629 \u0628\u062D\u062B \u0627\u0644\u0646\u0642\u0637\u0629 \u0627\u0644\u0623\u0642\u0631\u0628 \u0647\u064A \u0645\u0633\u0623\u0644\u0629 \u0631\u064A\u0627\u0636\u064A\u0629 \u0644\u0625\u064A\u062C\u0627\u062F \u0623\u0642\u0631\u0628 \u0627\u0644\u0646\u0642\u0627\u0637 \u0645\u0646 \u0645\u062C\u0645\u0648\u0639\u0629 \u0646\u0642\u0627\u0637 \u0644\u0646\u0642\u0637\u0629 \u0645\u0639\u064A\u0646\u0629 \u0641\u064A \u0627\u0644\u0641\u0636\u0627\u0621 \u0627\u0644\u0645\u062A\u0631\u064A."@ar . . "\uCD5C\uADFC\uC811 \uC774\uC6C3 \uD0D0\uC0C9"@ko . "\u041F\u043E\u0448\u0443\u043A \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u0441\u0443\u0441\u0456\u0434\u0430"@uk . . . . . . . "\u0645\u0633\u0623\u0644\u0629 \u0628\u062D\u062B \u0627\u0644\u0646\u0642\u0637\u0629 \u0627\u0644\u0623\u0642\u0631\u0628 \u0647\u064A \u0645\u0633\u0623\u0644\u0629 \u0631\u064A\u0627\u0636\u064A\u0629 \u0644\u0625\u064A\u062C\u0627\u062F \u0623\u0642\u0631\u0628 \u0627\u0644\u0646\u0642\u0627\u0637 \u0645\u0646 \u0645\u062C\u0645\u0648\u0639\u0629 \u0646\u0642\u0627\u0637 \u0644\u0646\u0642\u0637\u0629 \u0645\u0639\u064A\u0646\u0629 \u0641\u064A \u0627\u0644\u0641\u0636\u0627\u0621 \u0627\u0644\u0645\u062A\u0631\u064A."@ar . . . . . . . . . . . . . . . . "\u0417\u0430\u0434\u0430\u0447\u0430 \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u0441\u0443\u0441\u0456\u0434\u0430 \u0454 \u0437\u0430\u0434\u0430\u0447\u0435\u044E \u043E\u043F\u0442\u0438\u043C\u0456\u0437\u0430\u0446\u0456\u0457, \u044F\u043A\u0430 \u043F\u043E\u043B\u044F\u0433\u0430\u0454 \u0443 \u0432\u0456\u0434\u0448\u0443\u043A\u0430\u043D\u043D\u0456 \u0443 \u043C\u043D\u043E\u0436\u0438\u043D\u0456 \u0435\u043B\u0435\u043C\u0435\u043D\u0442\u0456\u0432, \u0440\u043E\u0437\u0442\u0430\u0448\u043E\u0432\u0430\u043D\u0438\u0445 \u0443 \u0431\u0430\u0433\u0430\u0442\u043E\u0432\u0438\u043C\u0456\u0440\u043D\u043E\u043C\u0443 \u043C\u0435\u0442\u0440\u0438\u0447\u043D\u043E\u043C\u0443 \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u0456, \u0435\u043B\u0435\u043C\u0435\u043D\u0442\u0456\u0432, \u0431\u043B\u0438\u0437\u044C\u043A\u0438\u0445 \u0434\u043E \u0437\u0430\u0434\u0430\u043D\u043E\u0433\u043E, \u0432\u0456\u0434\u043F\u043E\u0432\u0456\u0434\u043D\u043E \u0434\u043E \u0437\u0430\u0434\u0430\u043D\u043E\u0457 \u0444\u0443\u043D\u043A\u0446\u0456\u0457 \u0431\u043B\u0438\u0437\u044C\u043A\u043E\u0441\u0442\u0456. \u0424\u043E\u0440\u043C\u0430\u043B\u044C\u043D\u043E \u0446\u044F \u0437\u0430\u0434\u0430\u0447\u0430 \u0441\u0442\u0430\u0432\u0438\u0442\u044C\u0441\u044F \u043D\u0430\u0441\u0442\u0443\u043F\u043D\u0438\u043C \u0447\u0438\u043D\u043E\u043C: \u043D\u0430\u0434\u0430\u043D\u043E \u043C\u043D\u043E\u0436\u0438\u043D\u0443 \u0442\u043E\u0447\u043E\u043A S \u0443 \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u0456 M \u0442\u0430 \u0442\u043E\u0447\u043A\u0443 q \u2208 M, \u043D\u0435\u043E\u0431\u0445\u0456\u0434\u043D\u043E \u0437\u043D\u0430\u0439\u0442\u0438 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u0443 \u0434\u043E q \u0442\u043E\u0447\u043A\u0443 \u0432 S. \u0414\u043E\u043D\u0430\u043B\u044C\u0434 \u041A\u043D\u0443\u0442 \u0432 \u041C\u0438\u0441\u0442\u0435\u0446\u0442\u0432\u0456 \u043F\u0440\u043E\u0433\u0440\u0430\u043C\u0443\u0432\u0430\u043D\u043D\u044F (\u0442\u043E\u043C 3, 1973) \u043D\u0430\u0437\u0432\u0430\u0432 \u0446\u0435 \u043F\u0440\u043E\u0431\u043B\u0435\u043C\u043E\u044E \u043F\u043E\u0448\u0442\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0434\u0456\u043B\u0435\u043D\u043D\u044F, \u043F\u043E\u0441\u0438\u043B\u0430\u044E\u0447\u0438\u0441\u044C \u043D\u0430 \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F \u0446\u0456\u0454\u0457 \u0437\u0430\u0434\u0430\u0447\u0456 \u0434\u043E \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u043F\u043E\u0448\u0442\u043E\u0432\u043E\u0433\u043E \u0432\u0456\u0434\u0434\u0456\u043B\u0435\u043D\u043D\u044F. \u041F\u0440\u044F\u043C\u0438\u043C \u0443\u0437\u0430\u0433\u0430\u043B\u044C\u043D\u0435\u043D\u043D\u044F\u043C \u0437\u0430\u0434\u0430\u0447\u0456 \u043F\u043E\u0448\u0443\u043A\u0443 \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u043E\u0433\u043E \u0441\u0443\u0441\u0456\u0434\u0430 \u0454 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043F\u043E\u0448\u0443\u043A\u0443 k-NN, \u044F\u043A\u0438\u0439 \u043F\u0440\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u0438\u0439 \u0434\u043B\u044F \u043F\u043E\u0448\u0443\u043A\u0443 k \u043D\u0430\u0439\u0431\u043B\u0438\u0436\u0447\u0438\u0445 \u0442\u043E\u0447\u043E\u043A."@uk . . . . . . . "Nearest neighbor search"@en . . . . . . "\u6700\u8FD1\u508D\u63A2\u7D22"@ja . . . . "Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set S of points in a space M and a query point q \u2208 M, find the closest point in S to q. Donald Knuth in vol. 3 of The Art of Computer Programming (1973) called it the post-office problem, referring to an application of assigning to a residence the nearest post office. A direct generalization of this problem is a k-NN search, where we need to find the k closest points. Most commonly M is a metric space and dissimilarity is expressed as a distance metric, which is symmetric and satisfies the triangle inequality. Even more common, M is taken to be the d-dimensional vector space where dissimilarity is measured using the Euclidean distance, Manhattan distance or other distance metric. However, the dissimilarity function can be arbitrary. One example is asymmetric Bregman divergence, for which the triangle inequality does not hold."@en . . . . . . . . . . . . . . "\uCD5C\uADFC\uC811 \uC774\uC6C3 \uD0D0\uC0C9(\uC601\uC5B4: nearest neighbor search)\uC740 \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 (\uB610\uB294 \uAC00\uC7A5 \uADFC\uC811\uD55C) \uC810\uC744 \uCC3E\uAE30 \uC704\uD55C \uCD5C\uC801\uD654 \uBB38\uC81C\uC774\uB2E4. \uADFC\uC811 \uD0D0\uC0C9(proximity search), \uC720\uC0AC\uB3C4 \uD0D0\uC0C9(similarity search), \uCD5C\uADFC\uC811 \uC810\uC30D \uBB38\uC81C(closest point search)\uB77C\uACE0\uB3C4 \uBD88\uB9B0\uB2E4. \uADFC\uC0AC(\u8FD1\u4F3C)\uB77C\uB294 \uAC1C\uB150\uC740 \uBCF4\uD3B8\uC801\uC73C\uB85C \uBB3C\uCCB4\uC640 \uBB3C\uCCB4\uAC00 \uB35C \uC720\uC0AC\uD560\uC218\uB85D \uADF8 \uD568\uC218\uC758 \uAC12\uC740 \uCEE4\uC9C0\uB294 \uC0C1\uC774(\u76F8\u7570) \uD568\uC218\uC5D0 \uC758\uD574\uC11C \uD45C\uD604\uB41C\uB2E4. \uC5C4\uBC00\uD558\uAC8C, \uCD5C\uADFC\uC811 \uC774\uC6C3 \uD0D0\uC0C9 \uBB38\uC81C\uB294 \uB2E4\uC74C\uACFC \uAC19\uC774 \uC815\uC758\uB41C\uB2E4: \uACF5\uAC04 M\uC5D0\uC11C\uC758 \uC810\uB4E4\uB85C \uC774\uB8E8\uC5B4\uC9C4 \uC9D1\uD569 S \uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C, \uCFFC\uB9AC\uC810 q \u2208 M\uC5D0 \uB300\uD574 S \uC548\uC5D0\uC11C \uAC00\uC7A5 q\uC640 \uAC00\uAE4C\uC6B4 \uC810\uC744 \uCC3E\uB294\uB2E4. \uB3C4\uB110\uB4DC \uCEE4\uB204\uC2A4\uB294 \uADF8\uC758 \uC800\uC11C \u300A\uCEF4\uD4E8\uD130 \uD504\uB85C\uADF8\uB798\uBC0D\uC758 \uC608\uC220\u300B(1973) \uC81C 3\uAD8C\uC5D0\uC11C \uC0AC\uB78C\uB4E4\uC758 \uAC70\uC8FC\uC9C0\uB97C \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 \uC6B0\uCCB4\uAD6D\uC5D0 \uBC30\uC815\uD558\uB294 \uD504\uB85C\uADF8\uB7A8\uC774\uB77C\uB294 \uC758\uBBF8\uC5D0\uC11C \uC774\uB97C \uC6B0\uCCB4\uAD6D \uBB38\uC81C\uB77C\uACE0 \uBA85\uBA85\uD588\uB2E4. \uC774 \uBB38\uC81C\uC758 \uC9C1\uC811\uC801\uC778 \uC77C\uBC18\uD654 \uBB38\uC81C\uB85C\uC368\uB294, k \uAC1C\uC758 \uAC00\uC7A5 \uAC00\uAE4C\uC6B4 \uC810\uC744 \uCC3E\uB294 K-\uCD5C\uADFC\uC811 \uC774\uC6C3 \uC54C\uACE0\uB9AC\uC998\uC774 \uC788\uB2E4. \uBCF4\uD3B8\uC801\uC73C\uB85C, M\uC740 \uAC70\uB9AC \uACF5\uAC04\uC774\uACE0 \uC0C1\uC774(\u76F8\u7570)\uB3C4\uB294 \uB300\uCE6D\uC131\uC744 \uAC16\uACE0 \uC0BC\uAC01 \uBD80\uB4F1\uC2DD\uC744 \uB9CC\uC871\uD558\uB294 \uAC70\uB9AC \uACC4\uCE21\uC5D0 \uC758\uD574 \uD45C\uD604\uB41C\uB2E4. \uC774\uBCF4\uB2E4\uB3C4 \uC77C\uBC18\uC801\uC73C\uB85C \uD45C\uD604\uD558\uC790\uBA74, M\uC740 d\uCC28\uC6D0\uC758 \uBCA1\uD130 \uACF5\uAC04\uC774\uACE0 \uC0C1\uC774\uB3C4\uB294 \uC720\uD074\uB9AC\uB4DC \uAC70\uB9AC, \uB9E8\uD574\uD2BC \uAC70\uB9AC \uB4F1\uC744 \uC0AC\uC6A9\uD574 \uACC4\uCE21\uD55C\uB2E4. \uADF8\uB7EC\uB098 \uC0C1\uC774\uD568\uC218\uB294 \uC784\uC758\uC131\uC744 \uAC16\uAE30 \uB54C\uBB38\uC5D0, \uC608\uB97C \uB4E4\uBA74 \uB300\uCE6D\uC131\uC744 \uB744\uC9C0 \uC54A\uACE0 \uC0BC\uAC01 \uBD80\uB4F1\uC2DD\uC744 \uB9CC\uC871\uD558\uC9C0 \uC54A\uB294 \uBE0C\uB808\uADF8\uB9CC \uBC1C\uC0B0(Bregman Divergence)\uB4F1\uC744 \uC0AC\uC6A9\uD558\uC5EC \uC815\uC758\uD560 \uC218 \uC788\uB2E4."@ko . "\u6700\u8FD1\u508D\u63A2\u7D22\uFF08\u82F1: Nearest neighbor search, NNS\uFF09\u306F\u3001\u8DDD\u96E2\u7A7A\u9593\u306B\u304A\u3051\u308B\u6700\u3082\u8FD1\u3044\u70B9\u3092\u63A2\u3059\u6700\u9069\u5316\u554F\u984C\u306E\u4E00\u7A2E\u3001\u3042\u308B\u3044\u306F\u305D\u306E\u89E3\u6CD5\u3002\u8FD1\u63A5\u63A2\u7D22\uFF08\u82F1: proximity search\uFF09\u3001\u985E\u4F3C\u63A2\u7D22\uFF08\u82F1: similarity search\uFF09\u3001\u6700\u8FD1\u70B9\u63A2\u7D22\uFF08\u82F1: closest point search\uFF09\u306A\u3069\u3068\u3082\u547C\u3076\u3002\u554F\u984C\u306F\u3059\u306A\u308F\u3061\u3001\u8DDD\u96E2\u7A7A\u9593 M \u306B\u304A\u3051\u308B\u70B9\u306E\u96C6\u5408 S \u304C\u3042\u308A\u3001\u30AF\u30A8\u30EA\u70B9 q \u2208 M \u304C\u3042\u308B\u3068\u304D\u3001S \u306E\u4E2D\u3067 q \u306B\u6700\u3082\u8FD1\u3044\u70B9\u3092\u63A2\u3059\u3001\u3068\u3044\u3046\u554F\u984C\u3067\u3042\u308B\u3002\u591A\u304F\u306E\u5834\u5408\u3001M \u306B\u306F d\u6B21\u5143\u306E\u30E6\u30FC\u30AF\u30EA\u30C3\u30C9\u7A7A\u9593\u304C\u63A1\u7528\u3055\u308C\u3001\u8DDD\u96E2\u306F\u30E6\u30FC\u30AF\u30EA\u30C3\u30C9\u8DDD\u96E2\u304B\u30DE\u30F3\u30CF\u30C3\u30BF\u30F3\u8DDD\u96E2\u3067\u6E2C\u5B9A\u3055\u308C\u308B\u3002\u4F4E\u6B21\u5143\u306E\u5834\u5408\u3068\u9AD8\u6B21\u5143\u306E\u5834\u5408\u3067\u7570\u306A\u308B\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u304C\u3068\u3089\u308C\u308B\u3002 \u30C9\u30CA\u30EB\u30C9\u30FB\u30AF\u30CC\u30FC\u30B9\u306F\u3001The Art of Computer Programming Vol.3\uFF081973\u5E74\uFF09\u3067\u3001\u3053\u308C\u3092\u90F5\u4FBF\u5C40\u306E\u554F\u984C\u3067\u8868\u3057\u305F\u3002\u3053\u308C\u306F\u3059\u306A\u308F\u3061\u3001\u3042\u308B\u4F4F\u6240\u306B\u6700\u3082\u8FD1\u3044\u90F5\u4FBF\u5C40\u3092\u6C42\u3081\u308B\u554F\u984C\u3067\u3042\u308B\u3002"@ja . "1123903963"^^ . . . . . . . . . . "La recherche des plus proches voisins, ou des k plus proches voisins, est un probl\u00E8me algorithmique classique. De fa\u00E7on informelle le probl\u00E8me consiste, \u00E9tant donn\u00E9 un point \u00E0 trouver, dans un ensemble d'autres points, quels sont les k plus proches. La recherche de voisinage est utilis\u00E9e dans de nombreux domaines, tels la reconnaissance de formes, le clustering, l'approximation de fonctions, la pr\u00E9diction de s\u00E9ries temporelles et m\u00EAme les algorithmes de compression (recherche d'un groupe de donn\u00E9es le plus proche possible du groupe de donn\u00E9es \u00E0 compresser pour minimiser l'apport d'information). C'est en particulier l'\u00E9tape principale de la m\u00E9thode des k plus proches voisins en apprentissage automatique."@fr . . . . . . . . . . . . . . . . . . . .