{"id":478919,"date":"2023-08-09T09:40:22","date_gmt":"2023-08-09T09:40:22","guid":{"rendered":""},"modified":"2023-09-05T11:17:48","modified_gmt":"2023-09-05T11:17:48","slug":"semi-supervised-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/semi-supervised-learning\/","title":{"rendered":"\u534a\u76d1\u7763\u5b66\u4e60"},"content":{"rendered":"<p>\u534a\u76d1\u7763\u5b66\u4e60\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u8303\u5f0f\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u540c\u65f6\u4f7f\u7528\u6807\u8bb0\u6570\u636e\u548c\u672a\u6807\u8bb0\u6570\u636e\u3002\u5b83\u5f25\u8865\u4e86\u5b8c\u5168\u4f9d\u8d56\u6807\u8bb0\u6570\u636e\u7684\u76d1\u7763\u5b66\u4e60\u4e0e\u5b8c\u5168\u4e0d\u4f7f\u7528\u6807\u8bb0\u6570\u636e\u7684\u65e0\u76d1\u7763\u5b66\u4e60\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u6a21\u578b\u5229\u7528\u5927\u91cf\u672a\u6807\u8bb0\u6570\u636e\u4ee5\u53ca\u8f83\u5c11\u7684\u6807\u8bb0\u6570\u636e\u6765\u5b9e\u73b0\u66f4\u597d\u7684\u6027\u80fd\u3002<\/p>\n<h2>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u8d77\u6e90\u548c\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u534a\u76d1\u7763\u5b66\u4e60\u8d77\u6e90\u4e8e 20 \u4e16\u7eaa\u7684\u6a21\u5f0f\u8bc6\u522b\u7814\u7a76\u300220 \u4e16\u7eaa 60 \u5e74\u4ee3\uff0c\u7814\u7a76\u4eba\u5458\u9996\u6b21\u63d0\u51fa\u4e86\u8fd9\u4e00\u60f3\u6cd5\uff0c\u4ed6\u4eec\u8ba4\u8bc6\u5230\u4f7f\u7528\u6807\u8bb0\u6570\u636e\u548c\u672a\u6807\u8bb0\u6570\u636e\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u6548\u7387\u300220 \u4e16\u7eaa 90 \u5e74\u4ee3\u672b\uff0c\u8fd9\u4e00\u672f\u8bed\u6b63\u5f0f\u786e\u7acb\uff0cYoshua Bengio \u7b49\u7814\u7a76\u4eba\u5458\u4ee5\u53ca\u8be5\u9886\u57df\u7684\u5176\u4ed6\u9886\u519b\u4eba\u7269\u505a\u51fa\u4e86\u91cd\u5927\u8d21\u732e\u3002<\/p>\n<h2>\u5173\u4e8e\u534a\u76d1\u7763\u5b66\u4e60\u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898<\/h2>\n<p>\u534a\u76d1\u7763\u5b66\u4e60\u5229\u7528\u6807\u8bb0\u6570\u636e\uff08\u7ed3\u679c\u5df2\u77e5\u7684\u4e00\u5c0f\u7ec4\u793a\u4f8b\uff09\u548c\u672a\u6807\u8bb0\u6570\u636e\uff08\u7ed3\u679c\u672a\u77e5\u7684\u4e00\u5927\u7ec4\u793a\u4f8b\uff09\u7684\u7ec4\u5408\u3002\u5b83\u5047\u8bbe\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e24\u79cd\u7c7b\u578b\u7684\u6570\u636e\u6765\u638c\u63e1\u6570\u636e\u7684\u5e95\u5c42\u7ed3\u6784\uff0c\u4ece\u800c\u4f7f\u6a21\u578b\u80fd\u591f\u4ece\u8f83\u5c0f\u7684\u6807\u8bb0\u793a\u4f8b\u4e2d\u66f4\u597d\u5730\u6982\u62ec\u3002<\/p>\n<h3>\u534a\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5<\/h3>\n<ol>\n<li><strong>\u81ea\u6211\u8bad\u7ec3<\/strong>\uff1a\u5c06\u672a\u6807\u8bb0\u7684\u6570\u636e\u8fdb\u884c\u5206\u7c7b\uff0c\u7136\u540e\u52a0\u5165\u5230\u8bad\u7ec3\u96c6\u4e2d\u3002<\/li>\n<li><strong>\u591a\u89c6\u56fe\u8bad\u7ec3<\/strong>\uff1a\u4f7f\u7528\u6570\u636e\u7684\u4e0d\u540c\u89c6\u56fe\u6765\u5b66\u4e60\u591a\u4e2a\u5206\u7c7b\u5668\u3002<\/li>\n<li><strong>\u8054\u5408\u8bad\u7ec3<\/strong>\uff1a\u5728\u4e0d\u540c\u7684\u968f\u673a\u6570\u636e\u5b50\u96c6\u4e0a\u8bad\u7ec3\u591a\u4e2a\u5206\u7c7b\u5668\uff0c\u7136\u540e\u5c06\u5176\u7ec4\u5408\u8d77\u6765\u3002<\/li>\n<li><strong>\u57fa\u4e8e\u56fe\u7684\u65b9\u6cd5<\/strong>\uff1a\u6570\u636e\u7684\u7ed3\u6784\u4ee5\u56fe\u5f62\u7684\u5f62\u5f0f\u8868\u793a\uff0c\u4ee5\u8bc6\u522b\u6807\u8bb0\u548c\u672a\u6807\u8bb0\u5b9e\u4f8b\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/li>\n<\/ol>\n<h2>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u5185\u90e8\u7ed3\u6784\uff1a\u5176\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>\u534a\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\u7684\u5de5\u4f5c\u539f\u7406\u662f\u67e5\u627e\u672a\u6807\u8bb0\u6570\u636e\u4e2d\u7684\u9690\u85cf\u7ed3\u6784\uff0c\u4ece\u800c\u589e\u5f3a\u4ece\u6807\u8bb0\u6570\u636e\u4e2d\u5b66\u4e60\u7684\u80fd\u529b\u3002\u8be5\u8fc7\u7a0b\u901a\u5e38\u6d89\u53ca\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li><strong>\u521d\u59cb\u5316<\/strong>\uff1a\u4ece\u4e00\u4e2a\u5c0f\u7684\u6807\u8bb0\u6570\u636e\u96c6\u548c\u4e00\u4e2a\u5927\u7684\u672a\u6807\u8bb0\u6570\u636e\u96c6\u5f00\u59cb\u3002<\/li>\n<li><strong>\u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u5bf9\u6807\u8bb0\u6570\u636e\u8fdb\u884c\u521d\u6b65\u8bad\u7ec3\u3002<\/li>\n<li><strong>\u672a\u6807\u8bb0\u6570\u636e\u7684\u5229\u7528<\/strong>\uff1a\u4f7f\u7528\u6a21\u578b\u9884\u6d4b\u672a\u6807\u8bb0\u6570\u636e\u7684\u7ed3\u679c\u3002<\/li>\n<li><strong>\u8fed\u4ee3\u7ec6\u5316<\/strong>\uff1a\u901a\u8fc7\u6dfb\u52a0\u53ef\u4fe1\u9884\u6d4b\u4f5c\u4e3a\u65b0\u7684\u6807\u8bb0\u6570\u636e\u6765\u5b8c\u5584\u6a21\u578b\u3002<\/li>\n<li><strong>\u6700\u7ec8\u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u8bad\u7ec3\u5b8c\u5584\u7684\u6a21\u578b\u4ee5\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/li>\n<\/ol>\n<h2>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<ul>\n<li><strong>\u6548\u7387<\/strong>\uff1a\u5229\u7528\u5927\u91cf\u73b0\u6210\u7684\u672a\u6807\u8bb0\u6570\u636e\u3002<\/li>\n<li><strong>\u6027\u4ef7\u6bd4\u9ad8<\/strong>\uff1a\u51cf\u5c11\u4e86\u6602\u8d35\u7684\u6807\u7b7e\u5de5\u4f5c\u7684\u9700\u8981\u3002<\/li>\n<li><strong>\u7075\u6d3b\u6027<\/strong>\uff1a\u9002\u7528\u4e8e\u5404\u79cd\u9886\u57df\u548c\u4efb\u52a1\u3002<\/li>\n<li><strong>\u6311\u6218<\/strong>\uff1a\u5904\u7406\u566a\u58f0\u6570\u636e\u548c\u9519\u8bef\u6807\u7b7e\u53ef\u80fd\u5f88\u590d\u6742\u3002<\/li>\n<\/ul>\n<h2>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u7c7b\u578b\uff1a\u8868\u683c\u548c\u5217\u8868<\/h2>\n<p>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u5404\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5206\u4e3a\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u65b9\u6cd5<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u751f\u6210\u6a21\u578b<\/td>\n<td>\u6570\u636e\u8054\u5408\u5206\u5e03\u6a21\u578b<\/td>\n<\/tr>\n<tr>\n<td>\u81ea\u5b66<\/td>\n<td>\u6a21\u578b\u6807\u8bb0\u81ea\u5df1\u7684\u6570\u636e<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u5b9e\u4f8b<\/td>\n<td>\u4f7f\u7528\u5e26\u6709\u90e8\u5206\u6807\u7b7e\u7684\u5b9e\u4f8b\u5305<\/td>\n<\/tr>\n<tr>\n<td>\u57fa\u4e8e\u56fe\u7684\u65b9\u6cd5<\/td>\n<td>\u5229\u7528\u56fe\u5f62\u8868\u793a\u6570\u636e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<h3>\u5e94\u7528\u9886\u57df<\/h3>\n<ul>\n<li>\u56fe\u50cf\u8bc6\u522b<\/li>\n<li>\u8bed\u97f3\u5206\u6790<\/li>\n<li>\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/li>\n<li>\u533b\u7597\u8bca\u65ad<\/li>\n<\/ul>\n<h3>\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6848<\/h3>\n<ul>\n<li><strong>\u95ee\u9898<\/strong>\uff1a\u672a\u6807\u8bb0\u6570\u636e\u4e2d\u7684\u566a\u58f0\u3002<br \/>\n<strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\u5229\u7528\u7f6e\u4fe1\u5ea6\u9608\u503c\u548c\u7a33\u5065\u7b97\u6cd5\u3002<\/li>\n<li><strong>\u95ee\u9898<\/strong>\uff1a\u5173\u4e8e\u6570\u636e\u5206\u5e03\u7684\u9519\u8bef\u5047\u8bbe\u3002<br \/>\n<strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\u5e94\u7528\u9886\u57df\u4e13\u4e1a\u77e5\u8bc6\u6765\u6307\u5bfc\u6a21\u578b\u9009\u62e9\u3002<\/li>\n<\/ul>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u5176\u4ed6\u4e0e\u540c\u7c7b\u4ea7\u54c1\u7684\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u76d1\u7763<\/th>\n<th>\u534a\u76d1\u7763<\/th>\n<th>\u65e0\u76d1\u7763<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5229\u7528\u6807\u8bb0\u6570\u636e<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u4e0d<\/td>\n<\/tr>\n<tr>\n<td>\u5229\u7528\u672a\u6807\u8bb0\u7684\u6570\u636e<\/td>\n<td>\u4e0d<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u590d\u6742\u6027\u548c\u6210\u672c<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u7f13\u548c<\/td>\n<td>\u4f4e\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u6027\u80fd\u6709\u9650\u6807\u7b7e<\/td>\n<td>\u4f4e\u7684<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u5404\u4e0d\u76f8\u540c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u534a\u76d1\u7763\u5b66\u4e60\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>\u534a\u76d1\u7763\u5b66\u4e60\u7684\u672a\u6765\u524d\u666f\u5149\u660e\uff0c\u6b63\u5728\u8fdb\u884c\u7684\u7814\u7a76\u91cd\u70b9\u662f\uff1a<\/p>\n<ul>\n<li>\u66f4\u597d\u7684\u964d\u566a\u7b97\u6cd5<\/li>\n<li>\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u96c6\u6210<\/li>\n<li>\u6269\u5927\u5728\u5404\u4e2a\u884c\u4e1a\u9886\u57df\u7684\u5e94\u7528<\/li>\n<li>\u589e\u5f3a\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u7684\u5de5\u5177<\/li>\n<\/ul>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e\u534a\u76d1\u7763\u5b66\u4e60\u5173\u8054\u8d77\u6765<\/h2>\n<p>\u50cf OneProxy \u63d0\u4f9b\u7684\u4ee3\u7406\u670d\u52a1\u5668\u5728\u534a\u76d1\u7763\u5b66\u4e60\u573a\u666f\u4e2d\u975e\u5e38\u6709\u7528\u3002\u5b83\u4eec\u53ef\u4ee5\u5e2e\u52a9\uff1a<\/p>\n<ul>\n<li>\u4ece\u5404\u79cd\u6765\u6e90\u6536\u96c6\u5927\u578b\u6570\u636e\u96c6\uff0c\u5c24\u5176\u662f\u5f53\u9700\u8981\u7ed5\u8fc7\u533a\u57df\u9650\u5236\u65f6\u3002<\/li>\n<li>\u5904\u7406\u654f\u611f\u6570\u636e\u65f6\u786e\u4fdd\u9690\u79c1\u548c\u5b89\u5168\u3002<\/li>\n<li>\u901a\u8fc7\u51cf\u5c11\u5ef6\u8fdf\u548c\u4fdd\u6301\u4e00\u81f4\u7684\u8fde\u63a5\u6765\u589e\u5f3a\u5206\u5e03\u5f0f\u5b66\u4e60\u7684\u6027\u80fd\u3002<\/li>\n<\/ul>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/label_propagation.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-Learn \u534a\u76d1\u7763\u5b66\u4e60\u6307\u5357<\/a><\/li>\n<li><a href=\"https:\/\/www.iro.umontreal.ca\/~bengioy\/yoshua_en\/research.html\" target=\"_new\" rel=\"noopener nofollow\">Yoshua Bengio \u7684\u534a\u76d1\u7763\u5b66\u4e60\u7814\u7a76<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/cn\/\" target=\"_new\" rel=\"noopener\">OneProxy \u7684\u5b89\u5168\u6570\u636e\u5904\u7406\u670d\u52a1<\/a><\/li>\n<\/ul>\n<p>\u901a\u8fc7\u63a2\u7d22\u534a\u76d1\u7763\u5b66\u4e60\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u672c\u7efc\u5408\u6307\u5357\u65e8\u5728\u8ba9\u8bfb\u8005\u4e86\u89e3\u5176\u6838\u5fc3\u539f\u7406\u3001\u65b9\u6cd5\u3001\u5e94\u7528\u548c\u672a\u6765\u524d\u666f\uff0c\u5305\u62ec\u5176\u4e0e OneProxy \u63d0\u4f9b\u7684\u670d\u52a1\u7684\u4e00\u81f4\u6027\u3002<\/p>","protected":false},"featured_media":470457,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478919","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Semi-Supervised Learning: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Semi-Supervised Learning?","answer":"<p>Semi-supervised learning is a machine learning approach that combines both labeled and unlabeled data in the training process. This hybrid method bridges the gap between supervised learning, which relies solely on labeled data, and unsupervised learning, which operates without any labeled data. By leveraging both types of data, semi-supervised learning often achieves better performance.<\/p>"},{"question":"What are the key features of Semi-Supervised Learning?","answer":"<p>The key features of semi-supervised learning include its efficiency in utilizing large amounts of readily available unlabeled data, cost-effectiveness in reducing the need for extensive labeling, flexibility across various domains, and challenges such as handling noisy data and incorrect labeling.<\/p>"},{"question":"How does Semi-Supervised Learning work?","answer":"<p>Semi-supervised learning works by initially training on a small labeled dataset and then utilizing predictions on the larger unlabeled data. Through iterative refinement and retraining, the model incorporates confident predictions as new labeled data, enhancing the overall accuracy of the model.<\/p>"},{"question":"What types of Semi-Supervised Learning exist?","answer":"<p>There are several approaches to semi-supervised learning, including Generative Models, Self-Learning, Multi-Instance learning, and Graph-Based Methods. These methods vary in how they model the underlying relationships between labeled and unlabeled data.<\/p>"},{"question":"What are some applications and problems of Semi-Supervised Learning?","answer":"<p>Semi-supervised learning finds applications in image recognition, speech analysis, natural language processing, and medical diagnosis. Common problems include noise in the unlabeled data and incorrect assumptions about data distribution, with solutions like confidence thresholding and applying domain expertise to guide model selection.<\/p>"},{"question":"How do Semi-Supervised Learning and proxy servers like OneProxy relate?","answer":"<p>Proxy servers like OneProxy can be associated with semi-supervised learning by assisting in collecting large datasets, ensuring privacy and security in handling sensitive data, and enhancing the performance of distributed learning by reducing latency.<\/p>"},{"question":"What are the future perspectives of Semi-Supervised Learning?","answer":"<p>The future of semi-supervised learning is promising with ongoing research in areas such as better algorithms for noise reduction, integration with deep learning frameworks, expansion across various industry sectors, and the development of tools for model interpretability.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478919","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478919\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/470457"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=478919"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}