{"id":479450,"date":"2023-08-09T10:40:25","date_gmt":"2023-08-09T10:40:25","guid":{"rendered":""},"modified":"2023-09-05T11:18:50","modified_gmt":"2023-09-05T11:18:50","slug":"unlabeled-data","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/unlabeled-data\/","title":{"rendered":"Etiketlenmemi\u015f veriler"},"content":{"rendered":"<p>Etiketlenmemi\u015f veriler, a\u00e7\u0131k a\u00e7\u0131klamalar\u0131n veya s\u0131n\u0131f etiketlerinin bulunmad\u0131\u011f\u0131 verileri ifade eder; bu da onu, her veri noktas\u0131na belirli bir kategorinin atand\u0131\u011f\u0131 etiketli verilerden farkl\u0131 k\u0131lar. Bu t\u00fcr veriler, makine \u00f6\u011freniminde, \u00f6zellikle de sistemin, verileri y\u00f6nlendirecek \u00f6nceden var olan etiketler olmadan veriler i\u00e7indeki kal\u0131plar\u0131 ve yap\u0131lar\u0131 ke\u015ffetmesi gereken denetimsiz \u00f6\u011frenme algoritmalar\u0131 ba\u011flam\u0131nda yayg\u0131n olarak kullan\u0131l\u0131r. Etiketlenmemi\u015f veriler, \u00e7e\u015fitli uygulamalarda \u00f6nemli bir rol oynayarak, yeni ve g\u00f6r\u00fclmemi\u015f verilere iyi bir \u015fekilde genelle\u015ftirilebilen g\u00fc\u00e7l\u00fc modellerin geli\u015ftirilmesine olanak tan\u0131r.<\/p>\n<h2>Etiketlenmemi\u015f Verinin K\u00f6keninin Tarihi ve \u0130lk Bahsedilmesi<\/h2>\n<p>Makine \u00f6\u011freniminde etiketlenmemi\u015f verilerin kullan\u0131lmas\u0131 kavram\u0131, yapay zeka ara\u015ft\u0131rmalar\u0131n\u0131n ilk g\u00fcnlerine kadar uzanmaktad\u0131r. Ancak 1990&#039;larda denetimsiz \u00f6\u011frenme algoritmalar\u0131n\u0131n y\u00fckseli\u015fiyle b\u00fcy\u00fck ilgi g\u00f6rd\u00fc. Etiketlenmemi\u015f verilerin kullan\u0131lmas\u0131ndan ilk bahsedilenlerden biri, veri noktalar\u0131n\u0131n \u00f6nceden tan\u0131mlanm\u0131\u015f herhangi bir kategori olmadan benzerliklere g\u00f6re grupland\u0131r\u0131ld\u0131\u011f\u0131 k\u00fcmeleme algoritmalar\u0131 ba\u011flam\u0131ndayd\u0131. Y\u0131llar ge\u00e7tik\u00e7e, b\u00fcy\u00fck \u00f6l\u00e7ekli veri toplaman\u0131n ortaya \u00e7\u0131kmas\u0131 ve daha geli\u015fmi\u015f makine \u00f6\u011frenimi tekniklerinin geli\u015ftirilmesiyle birlikte etiketlenmemi\u015f verilerin \u00f6nemi artt\u0131.<\/p>\n<h2>Etiketlenmemi\u015f Verilere \u0130li\u015fkin Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Etiketlenmemi\u015f veriler, denetimsiz \u00f6\u011frenme, yar\u0131 denetimli \u00f6\u011frenme ve transfer \u00f6\u011frenimi dahil olmak \u00fczere \u00e7e\u015fitli makine \u00f6\u011frenimi g\u00f6revlerinin ayr\u0131lmaz bir par\u00e7as\u0131n\u0131 olu\u015fturur. Denetimsiz \u00f6\u011frenme algoritmalar\u0131, temel kal\u0131plar\u0131 bulmak, benzer veri noktalar\u0131n\u0131 gruplamak veya verilerin boyutunu azaltmak i\u00e7in etiketlenmemi\u015f verileri kullan\u0131r. Yar\u0131 denetimli \u00f6\u011frenme, daha do\u011fru modeller olu\u015fturmak i\u00e7in hem etiketli hem de etiketsiz verileri birle\u015ftirir; transfer \u00f6\u011frenimi ise etiketli verilerle bir g\u00f6revden \u00f6\u011frenilen bilgileri kullan\u0131r ve bunu s\u0131n\u0131rl\u0131 etiketli verilerle ba\u015fka bir g\u00f6reve uygular.<\/p>\n<p>Etiketlenmemi\u015f verilerin kullan\u0131m\u0131, do\u011fal dil i\u015fleme, bilgisayarl\u0131 g\u00f6rme ve di\u011fer alanlarda bir\u00e7ok ilerlemeye yol a\u00e7m\u0131\u015ft\u0131r. \u00d6rne\u011fin, Word2Vec ve GloVe gibi s\u00f6zc\u00fck yerle\u015ftirmeler, anlamsal ili\u015fkileri yakalayan s\u00f6zc\u00fck temsilleri olu\u015fturmak i\u00e7in b\u00fcy\u00fck miktarda etiketsiz metin \u00fczerinde e\u011fitilir. Benzer \u015fekilde, denetlenmeyen g\u00f6r\u00fcnt\u00fc temsilleri, \u00f6zellik temsillerini \u00f6\u011frenmede etiketlenmemi\u015f verilerin g\u00fcc\u00fc sayesinde g\u00f6r\u00fcnt\u00fc tan\u0131ma g\u00f6revlerini geli\u015ftirmi\u015ftir.<\/p>\n<h2>Etiketsiz Verinin \u0130\u00e7 Yap\u0131s\u0131: Etiketsiz Veri Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Etiketlenmemi\u015f veriler genellikle herhangi bir a\u00e7\u0131k a\u00e7\u0131klama veya kategori etiketi bulunmayan ham veri \u00f6rneklerinden veya \u00f6rneklerinden olu\u015fur. Bu veri noktalar\u0131 metin, resim, ses veya say\u0131sal veri gibi \u00e7e\u015fitli formatlarda olabilir. Makine \u00f6\u011freniminde etiketlenmemi\u015f verileri kullanman\u0131n amac\u0131, algoritman\u0131n anlaml\u0131 temsilleri \u00f6\u011frenmesini veya benzer veri noktalar\u0131n\u0131 k\u00fcmelemesini sa\u011flamak i\u00e7in verilerde mevcut olan do\u011fal kal\u0131plardan ve yap\u0131lardan yararlanmakt\u0131r.<\/p>\n<p>Etiketlenmemi\u015f veriler genellikle e\u011fitim s\u0131ras\u0131nda model performans\u0131n\u0131 art\u0131rmak i\u00e7in etiketli verilerle birle\u015ftirilir. Baz\u0131 durumlarda, etiketlenmemi\u015f verilerden olu\u015fan b\u00fcy\u00fck bir veri k\u00fcmesi \u00fczerinde denetimsiz \u00f6n e\u011fitim ger\u00e7ekle\u015ftirilir ve ard\u0131ndan etiketli verilerden olu\u015fan daha k\u00fc\u00e7\u00fck bir veri k\u00fcmesi \u00fczerinde denetimli ince ayar yap\u0131l\u0131r. Bu s\u00fcre\u00e7, modelin etiketlenmemi\u015f verilerden yararl\u0131 \u00f6zellikleri \u00f6\u011frenmesine olanak tan\u0131r ve bu \u00f6zellikler daha sonra etiketli verileri kullanarak belirli g\u00f6revlere g\u00f6re ince ayar yap\u0131labilir.<\/p>\n<h2>Etiketlenmemi\u015f Verilerin Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Etiketlenmemi\u015f verilerin temel \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li>A\u00e7\u0131k s\u0131n\u0131f etiketlerinin eksikli\u011fi: Her veri noktas\u0131n\u0131n belirli bir kategoriyle ili\u015fkilendirildi\u011fi etiketli verilerden farkl\u0131 olarak, etiketlenmemi\u015f verilerin \u00f6nceden tan\u0131mlanm\u0131\u015f etiketleri yoktur.<\/li>\n<li>Bolluk: Etiketlenmemi\u015f veriler, maliyetli a\u00e7\u0131klama \u00e7abalar\u0131na gerek kalmadan \u00e7e\u015fitli kaynaklardan toplanabildi\u011fi i\u00e7in genellikle b\u00fcy\u00fck miktarlarda kolayca bulunur.<\/li>\n<li>\u00c7e\u015fitlilik: Etiketlenmemi\u015f veriler, etiketli veri k\u00fcmelerinde yakalanamayan ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 yans\u0131tarak \u00e7ok \u00e7e\u015fitli varyasyonlar\u0131 ve karma\u015f\u0131kl\u0131klar\u0131 temsil edebilir.<\/li>\n<li>G\u00fcr\u00fclt\u00fc: Etiketlenmemi\u015f veriler \u00e7e\u015fitli kaynaklardan toplanabilece\u011finden g\u00fcr\u00fclt\u00fc ve tutars\u0131zl\u0131klar i\u00e7erebilir, bu da makine \u00f6\u011frenimi modellerinde kullan\u0131lmadan \u00f6nce dikkatli bir \u00f6n i\u015fleme gerektirir.<\/li>\n<\/ul>\n<h2>Etiketlenmemi\u015f Veri T\u00fcrleri<\/h2>\n<p>Her biri makine \u00f6\u011freniminde farkl\u0131 ama\u00e7lara hizmet eden \u00e7e\u015fitli etiketlenmemi\u015f veri t\u00fcrleri vard\u0131r:<\/p>\n<ol>\n<li>\n<p>Ham Etiketlenmemi\u015f Veriler: Bu, do\u011frudan web kaz\u0131ma, sens\u00f6r verileri veya kullan\u0131c\u0131 etkile\u015fimleri gibi kaynaklardan toplanan i\u015flenmemi\u015f verileri i\u00e7erir.<\/p>\n<\/li>\n<li>\n<p>\u00d6nceden \u0130\u015flenmi\u015f Etiketlenmemi\u015f Veriler: Bu t\u00fcr veriler belirli d\u00fczeyde temizlik ve d\u00f6n\u00fc\u015f\u00fcme tabi tutularak makine \u00f6\u011frenimi g\u00f6revleri i\u00e7in daha uygun hale getirilmi\u015ftir.<\/p>\n<\/li>\n<li>\n<p>Sentetik Etiketlenmemi\u015f Veri: Olu\u015fturulan veya sentetik veriler, mevcut etiketlenmemi\u015f veri k\u00fcmesini geni\u015fletmek ve model genellemesini geli\u015ftirmek i\u00e7in yapay olarak olu\u015fturulur.<\/p>\n<\/li>\n<\/ol>\n<h2>Etiketlenmemi\u015f Verileri Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>Etiketlenmemi\u015f verileri kullanma yollar\u0131:<\/p>\n<ol>\n<li>\n<p>Denetimsiz \u00d6\u011frenme: Etiketlenmemi\u015f veriler, \u00f6nceden tan\u0131mlanm\u0131\u015f herhangi bir etiket olmadan veriler i\u00e7indeki kal\u0131plar\u0131 ve yap\u0131lar\u0131 ke\u015ffetmek i\u00e7in kullan\u0131l\u0131r.<\/p>\n<\/li>\n<li>\n<p>Transfer \u00d6\u011frenimi i\u00e7in \u00d6n E\u011fitim: Etiketlenmemi\u015f veriler, daha k\u00fc\u00e7\u00fck etiketli veri k\u00fcmelerini kullanarak belirli g\u00f6revler i\u00e7in bunlara ince ayar yapmadan \u00f6nce b\u00fcy\u00fck veri k\u00fcmelerindeki modelleri \u00f6nceden e\u011fitmek i\u00e7in kullan\u0131l\u0131r.<\/p>\n<\/li>\n<li>\n<p>Veri Artt\u0131rma: Etiketlenmemi\u015f veriler sentetik \u00f6rnekler olu\u015fturmak, etiketli veri k\u00fcmesini geni\u015fletmek ve model sa\u011flaml\u0131\u011f\u0131n\u0131 geli\u015ftirmek i\u00e7in kullan\u0131labilir.<\/p>\n<\/li>\n<\/ol>\n<p>Etiketlenmemi\u015f verilerin kullan\u0131m\u0131na ili\u015fkin sorunlar ve \u00e7\u00f6z\u00fcmleri:<\/p>\n<ol>\n<li>\n<p>Temel Ger\u00e7e\u011fin Olmamas\u0131: Etiketlenmi\u015f temel ger\u00e7e\u011fin yoklu\u011fu, model performans\u0131n\u0131 objektif olarak de\u011ferlendirmeyi zorla\u015ft\u0131r\u0131r. Bu sorun, k\u00fcmeleme \u00f6l\u00e7\u00fcmleri kullan\u0131larak veya mevcut oldu\u011funda etiketli verilerden yararlan\u0131larak \u00e7\u00f6z\u00fclebilir.<\/p>\n<\/li>\n<li>\n<p>Veri Kalitesi: Etiketlenmemi\u015f veriler g\u00fcr\u00fclt\u00fc, ayk\u0131r\u0131 de\u011ferler veya eksik de\u011ferler i\u00e7erebilir ve bu da model performans\u0131n\u0131 olumsuz y\u00f6nde etkileyebilir. Dikkatli veri \u00f6n i\u015fleme ve ayk\u0131r\u0131 de\u011fer tespit teknikleri bu sorunu azaltabilir.<\/p>\n<\/li>\n<li>\n<p>A\u015f\u0131r\u0131 Uyum: Modellerin b\u00fcy\u00fck miktarlarda etiketlenmemi\u015f veriler \u00fczerinde e\u011fitilmesi, a\u015f\u0131r\u0131 uyumun ortaya \u00e7\u0131kmas\u0131na neden olabilir. D\u00fczenlile\u015ftirme teknikleri ve iyi tan\u0131mlanm\u0131\u015f mimariler bu sorunun \u00f6nlenmesine yard\u0131mc\u0131 olabilir.<\/p>\n<\/li>\n<\/ol>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>Terim<\/th>\n<th>\u00d6zellikler<\/th>\n<th>Etiketlenmemi\u015f Verilerden Fark\u0131<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Etiketli Veriler<\/td>\n<td>Her veri noktas\u0131n\u0131n a\u00e7\u0131k s\u0131n\u0131f etiketleri vard\u0131r.<\/td>\n<td>Etiketlenmemi\u015f verilerde \u00f6nceden tan\u0131mlanm\u0131\u015f kategori atamalar\u0131 yoktur.<\/td>\n<\/tr>\n<tr>\n<td>Yar\u0131 Denetimli \u00d6\u011frenme<\/td>\n<td>Hem etiketli hem de etiketsiz verileri kullan\u0131r.<\/td>\n<td>Etiketlenmemi\u015f veriler \u00f6\u011frenme kal\u0131plar\u0131na katk\u0131da bulunur.<\/td>\n<\/tr>\n<tr>\n<td>Denetimli \u00d6\u011frenme<\/td>\n<td>Yaln\u0131zca etiketlenmi\u015f verilere dayan\u0131r.<\/td>\n<td>Etiketlenmemi\u015f verileri e\u011fitim i\u00e7in kullanmaz.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Etiketlenmemi\u015f Verilere \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Makine \u00f6\u011freniminde etiketlenmemi\u015f verilerin gelece\u011fi \u00fcmit vericidir. Etiketlenmemi\u015f veri miktar\u0131 katlanarak artmaya devam ettik\u00e7e, daha geli\u015fmi\u015f denetimsiz \u00f6\u011frenme algoritmalar\u0131n\u0131n ve yar\u0131 denetimli tekniklerin ortaya \u00e7\u0131kmas\u0131 muhtemeldir. Ek olarak, veri art\u0131rma ve sentetik veri olu\u015fturmada devam eden ilerlemeyle birlikte, etiketlenmemi\u015f veriler \u00fczerinde e\u011fitilen modeller geli\u015fmi\u015f genelleme ve sa\u011flaml\u0131k sergileyebilir.<\/p>\n<p>Ayr\u0131ca, etiketlenmemi\u015f verilerin takviyeli \u00f6\u011frenme ve di\u011fer \u00f6\u011frenme paradigmalar\u0131yla birle\u015fimi, karma\u015f\u0131k ger\u00e7ek d\u00fcnya sorunlar\u0131n\u0131n \u00fcstesinden gelmek i\u00e7in b\u00fcy\u00fck bir potansiyele sahiptir. Yapay zeka ara\u015ft\u0131rmalar\u0131 ilerledik\u00e7e etiketlenmemi\u015f verilerin rol\u00fc, makine \u00f6\u011frenimi yeteneklerinin s\u0131n\u0131rlar\u0131n\u0131 zorlamada etkili olmaya devam edecek.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Etiketlenmemi\u015f Verilerle Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>Proxy sunucular, etiketlenmemi\u015f verilerin toplanmas\u0131n\u0131 kolayla\u015ft\u0131rmada hayati bir rol oynar. Kullan\u0131c\u0131lar ile internet aras\u0131nda arac\u0131 g\u00f6revi g\u00f6rerek kullan\u0131c\u0131lar\u0131n web i\u00e7eri\u011fine anonim olarak eri\u015fmesine ve i\u00e7erik k\u0131s\u0131tlamalar\u0131n\u0131 a\u015fmas\u0131na olanak tan\u0131r. Etiketlenmemi\u015f veriler ba\u011flam\u0131nda, proxy sunucular web sayfalar\u0131n\u0131 kaz\u0131mak, kullan\u0131c\u0131 etkile\u015fimlerini toplamak ve di\u011fer a\u00e7\u0131klamas\u0131z veri t\u00fcrlerini toplamak i\u00e7in kullan\u0131labilir.<\/p>\n<p>OneProxy (oneproxy.pro) gibi proxy sunucu sa\u011flay\u0131c\u0131lar\u0131, kullan\u0131c\u0131lar\u0131n geni\u015f bir IP adresi havuzuna eri\u015fmesine olanak tan\u0131yan hizmetler sunarak, anonimli\u011fi korurken veri toplamada \u00e7e\u015fitlilik sa\u011flar. Proxy sunucular\u0131n\u0131n veri toplama hatlar\u0131yla entegrasyonu, makine \u00f6\u011frenimi uygulay\u0131c\u0131lar\u0131n\u0131n e\u011fitim ve ara\u015ft\u0131rma amac\u0131yla kapsaml\u0131 etiketlenmemi\u015f veri k\u00fcmelerini toplamas\u0131na olanak tan\u0131r.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Etiketlenmemi\u015f Veriler hakk\u0131nda daha fazla bilgi i\u00e7in l\u00fctfen a\u015fa\u011f\u0131daki kaynaklara bak\u0131n:<\/p>\n<ol>\n<li><a href=\"https:\/\/www.example.com\/unlabeled-data-guide\" target=\"_new\" rel=\"noopener nofollow\">Makine \u00d6\u011freniminde Etiketlenmemi\u015f Veriler: Kapsaml\u0131 Bir K\u0131lavuz<\/a><\/li>\n<li><a href=\"https:\/\/www.example.com\/unsupervised-learning\" target=\"_new\" rel=\"noopener nofollow\">Denetimsiz \u00d6\u011frenme: Genel Bak\u0131\u015f<\/a><\/li>\n<li><a href=\"https:\/\/www.example.com\/semi-supervised-learning\" target=\"_new\" rel=\"noopener nofollow\">Yar\u0131 Denetimli \u00d6\u011frenme A\u00e7\u0131klamas\u0131<\/a><\/li>\n<\/ol>\n<p>Makine \u00f6\u011frenimi, etiketlenmemi\u015f verilerden yararlanarak \u00f6nemli ilerlemeler kaydetmeye devam ediyor ve gelecek, bu alanda \u00e7ok daha heyecan verici geli\u015fmeler vaat ediyor. Ara\u015ft\u0131rmac\u0131lar ve uygulay\u0131c\u0131lar etiketlenmemi\u015f verilerin potansiyelini daha derinlemesine ara\u015ft\u0131rd\u0131k\u00e7a, \u015f\u00fcphesiz en ileri yapay zeka uygulamalar\u0131n\u0131n temel ta\u015f\u0131 olmaya devam edecek.<\/p>","protected":false},"featured_media":479451,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479450","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Unlabeled Data: A Comprehensive Overview<\/mark>","faq_items":[{"question":"What is unlabeled data?","answer":"<p>Unlabeled data refers to data that lacks explicit annotations or class labels, making it different from labeled data, where each data point is assigned a specific category. It plays a crucial role in unsupervised learning algorithms, enabling the system to discover patterns and structures within the data without any pre-existing labels to guide it.<\/p>"},{"question":"How did the concept of using unlabeled data originate?","answer":"<p>The concept of using unlabeled data in machine learning dates back to the early days of artificial intelligence research. It gained significant attention in the 1990s with the rise of unsupervised learning algorithms. One of the earliest mentions was in the context of clustering algorithms, where data points are grouped based on similarities without predefined categories.<\/p>"},{"question":"What is the importance of unlabeled data in machine learning?","answer":"<p>Unlabeled data is essential in various machine learning tasks, including unsupervised learning, semi-supervised learning, and transfer learning. It helps in discovering patterns, creating meaningful representations, and improving model generalization, leading to breakthroughs in natural language processing, computer vision, and more.<\/p>"},{"question":"How does unlabeled data work?","answer":"<p>Unlabeled data consists of raw data samples without explicit labels. Machine learning algorithms leverage the inherent patterns and structures in this data to learn meaningful representations or cluster similar data points. Unlabeled data is often combined with labeled data during training to enhance model performance.<\/p>"},{"question":"What are the key features of unlabeled data?","answer":"<p>The key features of unlabeled data include its lack of explicit class labels, abundance in quantity, diversity in representing variations, and the possibility of containing noise and inconsistencies.<\/p>"},{"question":"What types of unlabeled data exist?","answer":"<p>There are three main types of unlabeled datraw unlabeled data, preprocessed unlabeled data, and synthetic unlabeled data. Raw data is unprocessed, preprocessed data undergoes cleaning and transformation, and synthetic data is artificially generated.<\/p>"},{"question":"How is unlabeled data used in machine learning?","answer":"<p>Unlabeled data is used in various ways, including unsupervised learning, pretraining for transfer learning, and data augmentation to create synthetic examples and enhance model robustness.<\/p>"},{"question":"What are the challenges related to using unlabeled data?","answer":"<p>The challenges include the absence of labeled ground truth for objective evaluation, data quality issues, and the risk of overfitting. These challenges can be addressed through proper evaluation metrics, data preprocessing, and regularization techniques.<\/p>"},{"question":"How does the future of unlabeled data look like in AI?","answer":"<p>The future of unlabeled data in machine learning is promising. As data continues to grow, advanced unsupervised learning algorithms and new learning paradigms are likely to emerge, leading to even more powerful AI models.<\/p>"},{"question":"How are proxy servers associated with unlabeled data?","answer":"<p>Proxy servers play a significant role in collecting unlabeled data by enabling anonymous web access and content scraping. They aid in data collection diversity and are often integrated with data pipelines for efficient data gathering.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/479451"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}