. . . . . . . . "\u041E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u0430\u043C"@ru . . . . . . . . . "visible hidden?"@en . . . . . . . . "En apprentissage automatique, l'apprentissage des caract\u00E9ristiques ou apprentissage des repr\u00E9sentations est un ensemble de techniques qui permet \u00E0 un syst\u00E8me de d\u00E9couvrir automatiquement les repr\u00E9sentations n\u00E9cessaires \u00E0 la d\u00E9tection ou \u00E0 la classification des caract\u00E9ristiques \u00E0 partir de donn\u00E9es brutes. Cela remplace l'ing\u00E9nierie manuelle des fonctionnalit\u00E9s et permet \u00E0 une machine d'apprendre les fonctionnalit\u00E9s et de les utiliser pour effectuer une t\u00E2che sp\u00E9cifique."@fr . "\u041E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u0430\u043C \u0438\u043B\u0438 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u0438\u044F\u043C \u2014 \u044D\u0442\u043E \u043D\u0430\u0431\u043E\u0440 \u0442\u0435\u0445\u043D\u0438\u043A, \u043A\u043E\u0442\u043E\u0440\u044B\u0435 \u043F\u043E\u0437\u0432\u043E\u043B\u044F\u044E\u0442 \u0441\u0438\u0441\u0442\u0435\u043C\u0435 \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u0438 \u043E\u0431\u043D\u0430\u0440\u0443\u0436\u0438\u0442\u044C \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u0438\u044F, \u043D\u0435\u043E\u0431\u0445\u043E\u0434\u0438\u043C\u044B\u0435 \u0434\u043B\u044F \u0432\u044B\u044F\u0432\u043B\u0435\u043D\u0438\u044F \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u043E\u0432 \u0438\u043B\u0438 \u043A\u043B\u0430\u0441\u0441\u0438\u0444\u0438\u043A\u0430\u0446\u0438\u0438 \u0438\u0441\u0445\u043E\u0434\u043D\u044B\u0445 (\u0441\u044B\u0440\u044B\u0445) \u0434\u0430\u043D\u043D\u044B\u0445. \u042D\u0442\u043E \u0437\u0430\u043C\u0435\u043D\u044F\u0435\u0442 \u0440\u0443\u0447\u043D\u043E\u0435 \u043A\u043E\u043D\u0441\u0442\u0440\u0443\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u0435 \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u043E\u0432 \u0438 \u043F\u043E\u0437\u0432\u043E\u043B\u044F\u0435\u0442 \u043C\u0430\u0448\u0438\u043D\u0435 \u043A\u0430\u043A \u0438\u0437\u0443\u0447\u0430\u0442\u044C \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u0438, \u0442\u0430\u043A \u0438 \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u043E\u0432\u0430\u0442\u044C \u0438\u0445 \u0434\u043B\u044F \u0440\u0435\u0448\u0435\u043D\u0438\u044F \u0441\u043F\u0435\u0446\u0438\u0444\u0438\u0447\u043D\u044B\u0445 \u0437\u0430\u0434\u0430\u0447. \u041E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0438\u0437\u043D\u0430\u043A\u0430\u043C \u043C\u043E\u0436\u0435\u0442 \u0431\u044B\u0442\u044C \u0441 \u0443\u0447\u0438\u0442\u0435\u043B\u0435\u043C \u0438\u043B\u0438 \u0431\u0435\u0437."@ru . . . . . . . "38870173"^^ . "\u0412 \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u043C\u0443 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u0456 \u043D\u0430\u0432\u0447\u0430\u0301\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u0301\u043A (\u0430\u043D\u0433\u043B. feature learning) \u0430\u0431\u043E \u043D\u0430\u0432\u0447\u0430\u0301\u043D\u043D\u044F \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0301\u0432\u043B\u0435\u043D\u044C (\u0430\u043D\u0433\u043B. representation learning) \u2014 \u0446\u0435 \u043D\u0430\u0431\u0456\u0440 \u043C\u0435\u0442\u043E\u0434\u0438\u043A, \u0449\u043E \u0434\u043E\u0437\u0432\u043E\u043B\u044F\u0454 \u0441\u0438\u0441\u0442\u0435\u043C\u0456 \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u043D\u043E \u0432\u0438\u044F\u0432\u043B\u044F\u0442\u0438 \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u043D\u044F, \u043D\u0435\u043E\u0431\u0445\u0456\u0434\u043D\u0456 \u0434\u043B\u044F \u0432\u0438\u044F\u0432\u043B\u0435\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A, \u0430\u0431\u043E \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0443\u0432\u0430\u043D\u043D\u044F \u0437 \u0441\u0438\u0440\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u0412\u043E\u043D\u043E \u0437\u0430\u043C\u0456\u043D\u044E\u0454 \u0440\u0443\u0447\u043D\u0435 \u043A\u043E\u043D\u0441\u0442\u0440\u0443\u044E\u0432\u0430\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A \u0456 \u0434\u043E\u0437\u0432\u043E\u043B\u044F\u0454 \u043C\u0430\u0448\u0438\u043D\u0456 \u044F\u043A \u043D\u0430\u0432\u0447\u0430\u0442\u0438\u0441\u044F \u043E\u0437\u043D\u0430\u043A, \u0442\u0430\u043A \u0456 \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u0432\u0430\u0442\u0438 \u0457\u0445 \u0434\u043B\u044F \u0432\u0438\u043A\u043E\u043D\u0430\u043D\u043D\u044F \u043A\u043E\u043D\u043A\u0440\u0435\u0442\u043D\u043E\u0433\u043E \u0437\u0430\u0432\u0434\u0430\u043D\u043D\u044F. \u041D\u0435\u043E\u0431\u0445\u0456\u0434\u043D\u0456\u0441\u0442\u044C \u0443 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u0456 \u043E\u0437\u043D\u0430\u043A \u043E\u0431\u0443\u043C\u043E\u0432\u043B\u0435\u043D\u043E \u0442\u0438\u043C \u0444\u0430\u043A\u0442\u043E\u043C, \u0449\u043E \u0442\u0430\u043A\u0456 \u0437\u0430\u0432\u0434\u0430\u043D\u043D\u044F \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u0433\u043E \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F, \u044F\u043A \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0443\u0432\u0430\u043D\u043D\u044F, \u0447\u0430\u0441\u0442\u043E \u043F\u043E\u0442\u0440\u0435\u0431\u0443\u044E\u0442\u044C \u0432\u0445\u043E\u0434\u0443, \u0449\u043E \u0454 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u043D\u043E \u0442\u0430 \u043E\u0431\u0447\u0438\u0441\u043B\u044E\u0432\u0430\u043B\u044C\u043D\u043E \u0437\u0440\u0443\u0447\u043D\u0438\u043C \u0434\u043B\u044F \u043E\u0431\u0440\u043E\u0431\u043A\u0438. \u041F\u0440\u043E\u0442\u0435 \u0434\u0430\u043D\u0456 \u0440\u0435\u0430\u043B\u044C\u043D\u043E\u0433\u043E \u0441\u0432\u0456\u0442\u0443, \u0442\u0430\u043A\u0456 \u044F\u043A \u0437\u043E\u0431\u0440\u0430\u0436\u0435\u043D\u043D\u044F, \u0432\u0456\u0434\u0435\u043E \u0442\u0430 \u0434\u0430\u0432\u0430\u0447\u0435\u0432\u0456 \u0432\u0438\u043C\u0456\u0440\u044E\u0432\u0430\u043D\u043D\u044F, \u0449\u0435 \u043D\u0435 \u043F\u0456\u0434\u0434\u0430\u044E\u0442\u044C\u0441\u044F \u0441\u043F\u0440\u043E\u0431\u0430\u043C \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C\u0456\u0447\u043D\u043E\u0433\u043E \u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043D\u044F \u043A\u043E\u043D\u043A\u0440\u0435\u0442\u043D\u0438\u0445 \u043E\u0437\u043D\u0430\u043A. \u0410\u043B\u044C\u0442\u0435\u0440\u043D\u0430\u0442\u0438\u0432\u043E\u044E \u0454 \u0432\u0438\u044F\u0432\u043B\u044F\u0442\u0438 \u0442\u0430\u043A\u0456 \u043E\u0437\u043D\u0430\u043A\u0438 \u0430\u0431\u043E \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u043D\u044F \u0447\u0435\u0440\u0435\u0437 \u0434\u043E\u0441\u043B\u0456\u0434\u0436\u0435\u043D\u043D\u044F, \u043D\u0435 \u043F\u043E\u043A\u043B\u0430\u0434\u0430\u044E\u0447\u0438\u0441\u044C \u043D\u0430 \u044F\u0432\u043D\u0456 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C\u0438. \u041D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u0430\u0431\u043E \u043A\u0435\u0440\u043E\u0432\u0430\u043D\u0438\u043C, \u0430\u0431\u043E \u0441\u043F\u043E\u043D\u0442\u0430\u043D\u043D\u0438\u043C. \n* \u0423 \u043A\u0435\u0440\u043E\u0432\u0430\u043D\u043E\u043C\u0443 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u0456 \u043E\u0437\u043D\u0430\u043A \u043C\u0430\u0448\u0438\u043D\u0430 \u043D\u0430\u0432\u0447\u0430\u0454\u0442\u044C\u0441\u044F \u043E\u0437\u043D\u0430\u043A \u0456\u0437 \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F\u043C \u043C\u0456\u0447\u0435\u043D\u0438\u0445 \u0432\u0445\u043E\u0434\u043E\u0432\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u0414\u043E \u043F\u0440\u0438\u043A\u043B\u0430\u0434\u0456\u0432 \u043D\u0430\u043B\u0435\u0436\u0430\u0442\u044C \u043A\u0435\u0440\u043E\u0432\u0430\u043D\u0456 \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0456 \u043C\u0435\u0440\u0435\u0436\u0456, \u0431\u0430\u0433\u0430\u0442\u043E\u0448\u0430\u0440\u043E\u0432\u0438\u0439 \u043F\u0435\u0440\u0446\u0435\u043F\u0442\u0440\u043E\u043D \u0442\u0430 (\u043A\u0435\u0440\u043E\u0432\u0430\u043D\u0435) . \n* \u0423 \u0441\u043F\u043E\u043D\u0442\u0430\u043D\u043D\u043E\u043C\u0443 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u0456 \u043E\u0437\u043D\u0430\u043A \u043C\u0430\u0448\u0438\u043D\u0430 \u043D\u0430\u0432\u0447\u0430\u0454\u0442\u044C\u0441\u044F \u043E\u0437\u043D\u0430\u043A \u0437 \u043D\u0435\u043C\u0456\u0447\u0435\u043D\u0438\u043C\u0438 \u0432\u0445\u043E\u0434\u043E\u0432\u0438\u043C\u0438 \u0434\u0430\u043D\u0438\u043C\u0438. \u0414\u043E \u043F\u0440\u0438\u043A\u043B\u0430\u0434\u0456\u0432 \u043D\u0430\u043B\u0435\u0436\u0430\u0442\u044C \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u0441\u043B\u043E\u0432\u043D\u0438\u043A\u0430, , \u0430\u0432\u0442\u043E\u043A\u043E\u0434\u0443\u0432\u0430\u043B\u044C\u043D\u0438\u043A\u0438, \u0440\u043E\u0437\u043A\u043B\u0430\u0434 \u043C\u0430\u0442\u0440\u0438\u0446\u044C \u0442\u0430 \u0440\u0456\u0437\u043D\u0456 \u0432\u0438\u0434\u0438 \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0443\u0432\u0430\u043D\u043D\u044F."@uk . . . . . . . . . . "June 2017"@en . . "42961"^^ . . . . . . . . . . . . . . . . . . . . . . . . . . . . "In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Feature learning can be either supervised, unsupervised or self-supervised. \n* In supervised feature learning, features are learned using labeled input data. Labeled data includes input-label pairs where the input is given to the model and it must produce the ground truth label as the correct answer. This can be leveraged to generate feature representations with the model which result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. \n* In unsupervised feature learning, features are learned with unlabeled input data by analyzing the relationship between points in the dataset. Examples include dictionary learning, independent component analysis, matrix factorization and various forms of clustering. \n* In self-supervised feature learning, features are learned using unlabeled data like unsupervised learning, however input-label pairs are constructed from each data point, which enables learning the structure of the data through supervised methods such as gradient descent. Classical examples include word embeddings and autoencoders. SSL has since been applied to many modalities through the use of deep neural network architectures such as CNNs and Transformers."@en . . "\u5728\u673A\u5668\u5B66\u4E60\u4E2D\uFF0C\u7279\u5F81\u5B66\u4E60\u6216\u8868\u5F81\u5B66\u4E60\u662F\u5B66\u4E60\u4E00\u4E2A\u7279\u5F81\u7684\u6280\u672F\u7684\u96C6\u5408\uFF1A\u5C06\u539F\u59CB\u6570\u636E\u8F6C\u6362\u6210\u4E3A\u80FD\u591F\u88AB\u673A\u5668\u5B66\u4E60\u6765\u6709\u6548\u5F00\u53D1\u7684\u4E00\u79CD\u5F62\u5F0F\u3002\u5B83\u907F\u514D\u4E86\u624B\u52A8\u63D0\u53D6\u7279\u5F81\u7684\u9EBB\u70E6\uFF0C\u5141\u8BB8\u8BA1\u7B97\u673A\u5B66\u4E60\u4F7F\u7528\u7279\u5F81\u7684\u540C\u65F6\uFF0C\u4E5F\u5B66\u4E60\u5982\u4F55\u63D0\u53D6\u7279\u5F81\uFF1A\u5B66\u4E60\u5982\u4F55\u5B66\u4E60\u3002 \u673A\u5668\u5B66\u4E60\u4EFB\u52A1\uFF0C\u4F8B\u5982\u5206\u7C7B\u95EE\u9898\uFF0C\u901A\u5E38\u90FD\u8981\u6C42\u8F93\u5165\u5728\u6570\u5B66\u4E0A\u6216\u8005\u5728\u8BA1\u7B97\u4E0A\u90FD\u975E\u5E38\u4FBF\u4E8E\u5904\u7406\uFF0C\u5728\u8FD9\u6837\u7684\u524D\u63D0\u4E0B\uFF0C\u7279\u5F81\u5B66\u4E60\u5C31\u5E94\u8FD0\u800C\u751F\u4E86\u3002\u7136\u800C\uFF0C\u73B0\u5B9E\u4E16\u754C\u4E2D\u7684\u6570\u636E\uFF0C\u4F8B\u5982\u5716\u7247\u3001\u5F71\u7247\uFF0C\u4EE5\u53CA\u611F\u6E2C\u5668\u7684\u6E2C\u91CF\u503C\u90FD\u975E\u5E38\u7684\u8907\u96DC\u3001\u5197\u9577\u53C8\u591A\u8B8A\uFF0C\u5982\u4F55\u6709\u6548\u7684\u63D0\u53D6\u51FA\u7279\u5F81\u5E76\u4E14\u5C06\u5176\u8868\u8FBE\u51FA\u4F86\u6210\u70BA\u4E86\u4E00\u500B\u91CD\u8981\u6311\u6230\u3002\u4F20\u7EDF\u7684\u624B\u52A8\u63D0\u53D6\u7279\u5F81\u9700\u8981\u5927\u91CF\u7684\u4EBA\u529B\u5E76\u4E14\u4F9D\u8D56\u4E8E\u975E\u5E38\u4E13\u4E1A\u7684\u77E5\u8BC6\u3002\u540C\u65F6\uFF0C\u8FD8\u4E0D\u4FBF\u4E8E\u63A8\u5E7F\u3002\u8FD9\u5C31\u8981\u6C42\u7279\u5F81\u5B66\u4E60\u6280\u672F\u7684\u6574\u4F53\u8BBE\u8BA1\u975E\u5E38\u6709\u6548\uFF0C\u81EA\u52A8\u5316\uFF0C\u5E76\u4E14\u6613\u4E8E\u63A8\u5E7F\u3002 \u7279\u5F81\u5B66\u4E60\u53EF\u4EE5\u88AB\u5206\u4E3A\u4E24\u7C7B\uFF1A\u76D1\u7763\u7684\u548C\u65E0\u76D1\u7763\u7684\uFF0C\u7C7B\u4F3C\u4E8E\u673A\u5668\u5B66\u4E60\u3002 \n* \u5728\u76D1\u7763\u7279\u5F81\u5B66\u4E60\u4E2D\uFF0C\u88AB\u6807\u8BB0\u8FC7\u7684\u6570\u636E\u88AB\u5F53\u505A\u7279\u5F81\u7528\u6765\u5B66\u4E60\u3002\u4F8B\u5982\u795E\u7ECF\u7F51\u7EDC\uFF0C\u591A\u5C42\u611F\u77E5\u5668\uFF0C(\u76D1\u7763)\u5B57\u5178\u5B66\u4E60\u3002 \n* \u5728\u65E0\u76D1\u7763\u7279\u5F81\u5B66\u4E60\u4E2D\uFF0C\u672A\u88AB\u6807\u8BB0\u8FC7\u7684\u6570\u636E\u88AB\u5F53\u505A\u7279\u5F81\u7528\u6765\u5B66\u4E60\u3002\u4F8B\u5982(\u65E0\u76D1\u7763)\u5B57\u5178\u5B66\u4E60\uFF0C\u72EC\u7ACB\u6210\u5206\u5206\u6790\uFF0C\uFF0C\u77E9\u9635\u5206\u89E3 \uFF0C\u5404\u79CD\u805A\u7C7B\u5206\u6790\u53CA\u5176\u53D8\u5F62\u3002"@zh . . . . "\u041D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A"@uk . "\u0412 \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u043C\u0443 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u0456 \u043D\u0430\u0432\u0447\u0430\u0301\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u0301\u043A (\u0430\u043D\u0433\u043B. feature learning) \u0430\u0431\u043E \u043D\u0430\u0432\u0447\u0430\u0301\u043D\u043D\u044F \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0301\u0432\u043B\u0435\u043D\u044C (\u0430\u043D\u0433\u043B. representation learning) \u2014 \u0446\u0435 \u043D\u0430\u0431\u0456\u0440 \u043C\u0435\u0442\u043E\u0434\u0438\u043A, \u0449\u043E \u0434\u043E\u0437\u0432\u043E\u043B\u044F\u0454 \u0441\u0438\u0441\u0442\u0435\u043C\u0456 \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u043D\u043E \u0432\u0438\u044F\u0432\u043B\u044F\u0442\u0438 \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u0435\u043D\u043D\u044F, \u043D\u0435\u043E\u0431\u0445\u0456\u0434\u043D\u0456 \u0434\u043B\u044F \u0432\u0438\u044F\u0432\u043B\u0435\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A, \u0430\u0431\u043E \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0443\u0432\u0430\u043D\u043D\u044F \u0437 \u0441\u0438\u0440\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u0412\u043E\u043D\u043E \u0437\u0430\u043C\u0456\u043D\u044E\u0454 \u0440\u0443\u0447\u043D\u0435 \u043A\u043E\u043D\u0441\u0442\u0440\u0443\u044E\u0432\u0430\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A \u0456 \u0434\u043E\u0437\u0432\u043E\u043B\u044F\u0454 \u043C\u0430\u0448\u0438\u043D\u0456 \u044F\u043A \u043D\u0430\u0432\u0447\u0430\u0442\u0438\u0441\u044F \u043E\u0437\u043D\u0430\u043A, \u0442\u0430\u043A \u0456 \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u0432\u0430\u0442\u0438 \u0457\u0445 \u0434\u043B\u044F \u0432\u0438\u043A\u043E\u043D\u0430\u043D\u043D\u044F \u043A\u043E\u043D\u043A\u0440\u0435\u0442\u043D\u043E\u0433\u043E \u0437\u0430\u0432\u0434\u0430\u043D\u043D\u044F. \u041D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u043E\u0437\u043D\u0430\u043A \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u0430\u0431\u043E \u043A\u0435\u0440\u043E\u0432\u0430\u043D\u0438\u043C, \u0430\u0431\u043E \u0441\u043F\u043E\u043D\u0442\u0430\u043D\u043D\u0438\u043C."@uk . "En el aprendizaje autom\u00E1tico, el aprendizaje de caracter\u00EDsticas o aprendizaje de representaci\u00F3n\u200B es un conjunto de t\u00E9cnicas que permite que un sistema descubra autom\u00E1ticamente las representaciones necesarias para la detecci\u00F3n o clasificaci\u00F3n de caracter\u00EDsticas a partir de datos sin procesar. Esto reemplaza la ingenier\u00EDa de caracter\u00EDstica manual y permite que una m\u00E1quina aprenda caracter\u00EDsticas y las use para realizar una tarea espec\u00EDfica. El aprendizaje de caracter\u00EDsticas puede ser supervisado o no supervisado."@es . . . . . . "Apprentissage de repr\u00E9sentations"@fr . . . . . . . . . "En el aprendizaje autom\u00E1tico, el aprendizaje de caracter\u00EDsticas o aprendizaje de representaci\u00F3n\u200B es un conjunto de t\u00E9cnicas que permite que un sistema descubra autom\u00E1ticamente las representaciones necesarias para la detecci\u00F3n o clasificaci\u00F3n de caracter\u00EDsticas a partir de datos sin procesar. Esto reemplaza la ingenier\u00EDa de caracter\u00EDstica manual y permite que una m\u00E1quina aprenda caracter\u00EDsticas y las use para realizar una tarea espec\u00EDfica. El aprendizaje de caracter\u00EDstica est\u00E1 motivado por el hecho de que la m\u00E1quina que aprende tareas como la clasificaci\u00F3n a menudo requiere entrada que es matem\u00E1ticamente y computacionalmente conveniente de procesar. Aun as\u00ED, datos del mundo real como im\u00E1genes, v\u00EDdeos, y datos de sensor no permiten definir algor\u00EDtmicamente caracter\u00EDsticas espec\u00EDficas. Una alternativa es descubrir tales caracter\u00EDsticas o representaciones a trav\u00E9s de ex\u00E1menes, sin depender de algoritmos expl\u00EDcitos. El aprendizaje de caracter\u00EDsticas puede ser supervisado o no supervisado. \n* En el aprendizaje de caracter\u00EDsticas supervisado, las caracter\u00EDsticas se aprenden utilizando datos de entrada etiquetados. Los ejemplos incluyen redes neuronales supervisadas, perceptr\u00F3n multicapa y aprendizaje de diccionario (supervisado) . \n* En el aprendizaje de caracter\u00EDsticas no supervisado, las caracter\u00EDsticas se aprenden con datos de entrada sin etiqueta. Los ejemplos incluyen aprendizaje de diccionario, an\u00E1lisis de componentes independientes, autoencoders, factorizaci\u00F3n matricial\u200B y varias formas de agrupamiento.\u200B\u200B"@es . "En apprentissage automatique, l'apprentissage des caract\u00E9ristiques ou apprentissage des repr\u00E9sentations est un ensemble de techniques qui permet \u00E0 un syst\u00E8me de d\u00E9couvrir automatiquement les repr\u00E9sentations n\u00E9cessaires \u00E0 la d\u00E9tection ou \u00E0 la classification des caract\u00E9ristiques \u00E0 partir de donn\u00E9es brutes. Cela remplace l'ing\u00E9nierie manuelle des fonctionnalit\u00E9s et permet \u00E0 une machine d'apprendre les fonctionnalit\u00E9s et de les utiliser pour effectuer une t\u00E2che sp\u00E9cifique. L'apprentissage des fonctionnalit\u00E9s est motiv\u00E9 par le fait que les t\u00E2ches d'apprentissage automatique telles que la classification n\u00E9cessitent souvent des entr\u00E9es qui sont math\u00E9matiquement et informatiquement pratiques \u00E0 traiter. Cependant, les donn\u00E9es du monde r\u00E9el telles que les images, les vid\u00E9os et les donn\u00E9es de capteurs n'ont pas c\u00E9d\u00E9 aux tentatives de d\u00E9finition algorithmique de caract\u00E9ristiques sp\u00E9cifiques. Une alternative consiste \u00E0 d\u00E9couvrir ces caract\u00E9ristiques ou repr\u00E9sentations en examinant, sans s'appuyer sur des algorithmes explicites."@fr . . . . "\uD2B9\uC9D5 \uD559\uC2B5(feature learning) \uB610\uB294 \uD45C\uD604 \uD559\uC2B5(representation learning)\uC740 \uD2B9\uC9D5\uC744 \uC790\uB3D9\uC73C\uB85C \uCD94\uCD9C\uD560 \uC218 \uC788\uB3C4\uB85D \uD559\uC2B5\uD558\uB294 \uACFC\uC815\uC774\uB2E4. \uC9C0\uB3C4 \uD559\uC2B5\uACFC \uBE44\uC9C0\uB3C4 \uD559\uC2B5\uC73C\uB85C \uB098\uB20C \uC218 \uC788\uB2E4. \uD2B9\uC9D5 \uD559\uC2B5\uC740 \uD1B5\uACC4\uC801 \uBD84\uB958\uC640 \uAC19\uC740 \uBA38\uC2E0 \uB7EC\uB2DD \uACFC\uC81C\uAC00 \uC218\uD559\uC801\uC73C\uB85C\uB098 \uCEF4\uD4E8\uD130\uC0C1\uC5D0\uC11C \uCC98\uB9AC\uD558\uAE30 \uD3B8\uB9AC\uD55C \uC785\uB825\uC744 \uC885\uC885 \uC694\uAD6C\uD558\uAE30 \uB54C\uBB38\uC5D0 \uD544\uC694\uD558\uB2E4. \uADF8\uB7EC\uB098 \uD2B9\uC815\uD55C \uD2B9\uC9D5\uC744 \uC54C\uACE0\uB9AC\uC998\uC73C\uB85C \uC815\uC758\uD558\uB824\uB294 \uC2DC\uB3C4\uB294 \uC0AC\uC9C4, \uC601\uC0C1, \uAC10\uAC01 \uB370\uC774\uD130\uC640 \uAC19\uC740 \uC2E4\uC81C \uC138\uACC4\uC758 \uB370\uC774\uD130\uC5D0 \uB300\uD574\uC11C\uB294 \uC5B4\uB824\uC6C0\uC744 \uACAA\uC5C8\uB2E4. \uC774\uC5D0 \uB300\uD55C \uB300\uC548\uC73C\uB85C \uBA85\uC2DC\uC801\uC778 \uC54C\uACE0\uB9AC\uC998\uC5D0 \uC758\uC874\uD558\uC9C0 \uC54A\uACE0 \uC774\uB7EC\uD55C \uD2B9\uC9D5\uB4E4\uC744 \uAC80\uC0AC\uB97C \uD1B5\uD574 \uBC1C\uACAC\uD558\uB294 \uAC83\uC774 \uC81C\uC548\uB418\uC5C8\uB2E4. \uD2B9\uC9D5 \uD559\uC2B5\uC5D0\uB294 \uC9C0\uB3C4, \uBE44\uC9C0\uB3C4, \uC790\uAE30\uC9C0\uB3C4 \uD559\uC2B5\uC774 \uC788\uB2E4."@ko . . . . . . . . "1121391575"^^ . . . . . . . . . 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\u0432\u0445\u043E\u0434\u043D\u044B\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u041F\u0440\u0438\u043C\u0435\u0440\u0430\u043C\u0438 \u044F\u0432\u043B\u044F\u044E\u0442\u0441\u044F \u0441\u043B\u043E\u0432\u0430\u0440\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435, \u0430\u043D\u0430\u043B\u0438\u0437 \u043D\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043C\u044B\u0445 \u043A\u043E\u043C\u043F\u043E\u043D\u0435\u043D\u0442, \u0430\u0432\u0442\u043E\u043A\u043E\u0434\u0438\u0440\u043E\u0432\u0449\u0438\u043A\u0438, \u0440\u0430\u0437\u043B\u043E\u0436\u0435\u043D\u0438\u0435 \u043C\u0430\u0442\u0440\u0438\u0446 \u0438 \u0440\u0430\u0437\u043B\u0438\u0447\u043D\u044B\u0435 \u0444\u043E\u0440\u043C\u044B \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0438\u0437\u0430\u0446\u0438\u0438."@ru . . . . . . . "Feature learning"@en . "Aprendizaje de caracter\u00EDsticas"@es . . . . . . . . . . . . . . . . . . "\uD2B9\uC9D5 \uD559\uC2B5(feature learning) \uB610\uB294 \uD45C\uD604 \uD559\uC2B5(representation learning)\uC740 \uD2B9\uC9D5\uC744 \uC790\uB3D9\uC73C\uB85C \uCD94\uCD9C\uD560 \uC218 \uC788\uB3C4\uB85D \uD559\uC2B5\uD558\uB294 \uACFC\uC815\uC774\uB2E4. \uC9C0\uB3C4 \uD559\uC2B5\uACFC \uBE44\uC9C0\uB3C4 \uD559\uC2B5\uC73C\uB85C \uB098\uB20C \uC218 \uC788\uB2E4. \uD2B9\uC9D5 \uD559\uC2B5\uC740 \uD1B5\uACC4\uC801 \uBD84\uB958\uC640 \uAC19\uC740 \uBA38\uC2E0 \uB7EC\uB2DD \uACFC\uC81C\uAC00 \uC218\uD559\uC801\uC73C\uB85C\uB098 \uCEF4\uD4E8\uD130\uC0C1\uC5D0\uC11C \uCC98\uB9AC\uD558\uAE30 \uD3B8\uB9AC\uD55C \uC785\uB825\uC744 \uC885\uC885 \uC694\uAD6C\uD558\uAE30 \uB54C\uBB38\uC5D0 \uD544\uC694\uD558\uB2E4. \uADF8\uB7EC\uB098 \uD2B9\uC815\uD55C \uD2B9\uC9D5\uC744 \uC54C\uACE0\uB9AC\uC998\uC73C\uB85C \uC815\uC758\uD558\uB824\uB294 \uC2DC\uB3C4\uB294 \uC0AC\uC9C4, \uC601\uC0C1, \uAC10\uAC01 \uB370\uC774\uD130\uC640 \uAC19\uC740 \uC2E4\uC81C \uC138\uACC4\uC758 \uB370\uC774\uD130\uC5D0 \uB300\uD574\uC11C\uB294 \uC5B4\uB824\uC6C0\uC744 \uACAA\uC5C8\uB2E4. \uC774\uC5D0 \uB300\uD55C \uB300\uC548\uC73C\uB85C \uBA85\uC2DC\uC801\uC778 \uC54C\uACE0\uB9AC\uC998\uC5D0 \uC758\uC874\uD558\uC9C0 \uC54A\uACE0 \uC774\uB7EC\uD55C \uD2B9\uC9D5\uB4E4\uC744 \uAC80\uC0AC\uB97C \uD1B5\uD574 \uBC1C\uACAC\uD558\uB294 \uAC83\uC774 \uC81C\uC548\uB418\uC5C8\uB2E4. \uD2B9\uC9D5 \uD559\uC2B5\uC5D0\uB294 \uC9C0\uB3C4, \uBE44\uC9C0\uB3C4, \uC790\uAE30\uC9C0\uB3C4 \uD559\uC2B5\uC774 \uC788\uB2E4. \n* \uC9C0\uB3C4 \uD559\uC2B5\uC740 \uB808\uC774\uBE14\uC774 \uC788\uB294 \uC785\uB825\uAC12\uC744 \uC0AC\uC6A9\uD558\uC5EC \uD2B9\uC9D5\uC744 \uD559\uC2B5\uD558\uB294 \uAC83\uC774\uB2E4. \uB808\uC774\uBE14\uC774 \uC9C0\uC815\uB41C \uB370\uC774\uD130\uB294 \uC785\uB825-\uB808\uC774\uBE14 \uC30D\uC744 \uD3EC\uD568\uD558\uACE0 \uC788\uC73C\uBA70 \uC815\uB2F5\uC73C\uB85C \uC2E4\uCE21 \uC790\uB8CC \uB808\uC774\uBE14\uC744 \uB0B4\uB193\uC544\uC57C \uD55C\uB2E4. \uC9C0\uB3C4 \uD559\uC2B5\uC740 \uB192\uC740 \uB808\uC774\uBE14 \uC608\uCE21 \uC815\uD655\uB3C4\uB97C \uAC00\uC838\uC624\uB294 \uBAA8\uB378\uB85C \uD2B9\uC9D5 \uD45C\uD604\uC744 \uC0DD\uC131\uD558\uB294 \uB370 \uD65C\uC6A9\uD560 \uC218 \uC788\uB2E4. \uC751\uC6A9\uC758 \uC608\uC2DC\uB85C\uB294 \uC778\uACF5 \uC2E0\uACBD\uB9DD, \uB2E4\uCE35 \uD37C\uC149\uD2B8\uB860, \uC774 \uC788\uB2E4. \n* \uBE44\uC9C0\uB3C4 \uD559\uC2B5\uC740 \uB808\uC774\uBE14\uC774 \uC5C6\uB294 \uB370\uC774\uD130\uB97C \uC774\uC6A9\uD558\uC5EC \uB370\uC774\uD130 \uAC04\uC758 \uAD00\uACC4\uB97C \uBD84\uC11D\uD558\uB294 \uBC29\uC2DD\uC73C\uB85C \uD2B9\uC9D5\uC744 \uD559\uC2B5\uD55C\uB2E4. \uC0AC\uC804 \uD559\uC2B5, \uB3C5\uB9BD \uC131\uBD84 \uBD84\uC11D, \uD589\uB82C \uBD84\uD574, \uB2E4\uC591\uD55C \uD615\uD0DC\uC758 \uD074\uB7EC\uC2A4\uD130 \uBD84\uC11D\uC774 \uC5EC\uAE30 \uD3EC\uD568\uB41C\uB2E4. \n* \uC790\uAE30\uC9C0\uB3C4 \uD559\uC2B5\uC5D0\uC11C \uD2B9\uC9D5\uC740 \uBE44\uC9C0\uB3C4 \uD559\uC2B5\uCC98\uB7FC \uB808\uC774\uBE14\uC774 \uC5C6\uB294 \uB370\uC774\uD130\uB97C \uC0AC\uC6A9\uD558\uC5EC \uD559\uC2B5\uB41C\uB2E4. \uADF8\uB7EC\uB098 \uC785\uB825-\uB808\uC774\uBE14 \uC30D\uC740 \uAC01 \uB370\uC774\uD130 \uC9C0\uC810\uC5D0\uC11C \uB9CC\uB4E4\uC5B4\uC838, \uAE30\uC6B8\uAE30 \uD558\uAC15\uACFC \uAC19\uC740 \uC9C0\uB3C4 \uD559\uC2B5\uBC95\uC744 \uD1B5\uD574 \uB370\uC774\uD130 \uAD6C\uC870\uB97C \uD559\uC2B5\uD560 \uC218 \uC788\uAC8C \uB41C\uB2E4. \uACE0\uC804\uC801\uC778 \uC608\uC2DC\uB85C\uB294 , \uC624\uD1A0\uC778\uCF54\uB354\uAC00 \uC788\uB2E4. \uC790\uAE30\uC9C0\uB3C4 \uD559\uC2B5\uC740 \uD569\uC131\uACF1 \uC2E0\uACBD\uB9DD\uC774\uB098 \uBCC0\uD658\uAE30 \uB4F1\uC758 \uC2EC\uCE35 \uC2E0\uACBD\uB9DD\uC744 \uD1B5\uD574 \uB9CE\uC740 \uBD84\uC57C\uC5D0 \uC751\uC6A9\uB418\uC5B4 \uC654\uB2E4."@ko . "\u8868\u5F81\u5B66\u4E60"@zh . "In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning can be either supervised, unsupervised or self-supervised."@en . . . . . . . . . . . . . . . . "\uD2B9\uC9D5 \uD559\uC2B5"@ko . . . . .