{"id":479180,"date":"2023-08-09T10:31:59","date_gmt":"2023-08-09T10:31:59","guid":{"rendered":""},"modified":"2023-09-05T11:18:21","modified_gmt":"2023-09-05T11:18:21","slug":"structured-prediction","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/structured-prediction\/","title":{"rendered":"Yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin"},"content":{"rendered":"<p>Yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin, skaler ayr\u0131k veya ger\u00e7ek de\u011ferlerden ziyade yap\u0131land\u0131r\u0131lm\u0131\u015f nesnelerin tahmin edilmesi sorununu ifade eder. Makine \u00f6\u011freniminin bu alan\u0131 genellikle karma\u015f\u0131k kar\u015f\u0131l\u0131kl\u0131 ba\u011f\u0131ml\u0131l\u0131klara sahip birden fazla \u00e7\u0131kt\u0131n\u0131n tahmin edilmesiyle ilgilenir. Do\u011fal dil i\u015fleme, biyoenformatik, bilgisayarl\u0131 g\u00f6rme ve daha fazlas\u0131 gibi \u00e7e\u015fitli alanlarda yayg\u0131n olarak kullan\u0131lmaktad\u0131r. Yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin modelleri, bir \u00e7\u0131kt\u0131 yap\u0131s\u0131n\u0131n farkl\u0131 b\u00f6l\u00fcmleri aras\u0131ndaki ili\u015fkileri yakalar ve bunlar\u0131 yeni \u00f6rnekleri tahmin etmek i\u00e7in kullan\u0131r.<\/p>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahminin K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Yap\u0131land\u0131r\u0131lm\u0131\u015f tahminin k\u00f6kenleri istatistik ve makine \u00f6\u011frenimindeki ilk \u00e7al\u0131\u015fmalara kadar uzanabilir. 1990&#039;larda ara\u015ft\u0131rmac\u0131lar, basit skaler de\u011ferler yerine karma\u015f\u0131k yap\u0131l\u0131 nesneleri tahmin etme ihtiyac\u0131n\u0131 fark etmeye ba\u015flad\u0131lar. Bu, 2001 y\u0131l\u0131nda John Lafferty, Andrew McCallum ve Fernando Pereira taraf\u0131ndan Ko\u015fullu Rastgele Alanlar (CRF&#039;ler) gibi modellerin geli\u015ftirilmesine yol a\u00e7t\u0131 ve bu t\u00fcr sorunlar\u0131n \u00fcstesinden gelmede etkili oldu.<\/p>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahmin Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin, \u00f6\u011feleri aras\u0131nda tipik olarak ili\u015fkiler bulunan yap\u0131land\u0131r\u0131lm\u0131\u015f bir nesnenin (\u00f6rne\u011fin bir dizi, a\u011fa\u00e7 veya grafik) tahmin edilmesini i\u00e7erir. Yap\u0131land\u0131r\u0131lm\u0131\u015f tahminin temel bile\u015fenleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<h3>Modeller<\/h3>\n<ul>\n<li><strong>Grafiksel Modeller:<\/strong> CRF&#039;ler, Gizli Markov Modelleri (HMM&#039;ler) gibi.<\/li>\n<li><strong>Yap\u0131land\u0131r\u0131lm\u0131\u015f Destek Vekt\u00f6r Makineleri:<\/strong> Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar i\u00e7in SVM&#039;lerin genelle\u015ftirilmesi.<\/li>\n<\/ul>\n<h3>E\u011fitim<\/h3>\n<ul>\n<li><strong>Yap\u0131land\u0131r\u0131lm\u0131\u015f Kay\u0131p Fonksiyonlar\u0131:<\/strong> Tahmin edilen ve ger\u00e7ek yap\u0131lar aras\u0131ndaki fark\u0131 \u00f6l\u00e7meye y\u00f6nelik y\u00f6ntemler.<\/li>\n<li><strong>\u00c7\u0131kar\u0131m Algoritmalar\u0131:<\/strong> En olas\u0131 \u00e7\u0131kt\u0131 yap\u0131s\u0131n\u0131 bulmak i\u00e7in dinamik programlama, do\u011frusal programlama gibi teknikler.<\/li>\n<\/ul>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahminin \u0130\u00e7 Yap\u0131s\u0131: Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahmin Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Yap\u0131land\u0131r\u0131lm\u0131\u015f tahminin i\u015fleyi\u015fi a\u015fa\u011f\u0131daki ad\u0131mlarla anla\u015f\u0131labilir:<\/p>\n<ol>\n<li><strong>Giri\u015f G\u00f6sterimi:<\/strong> Ham verileri yap\u0131sal ba\u011f\u0131ml\u0131l\u0131klar\u0131 vurgulayan bir \u00f6zellik alan\u0131na e\u015fleme.<\/li>\n<li><strong>Kar\u015f\u0131l\u0131kl\u0131 Ba\u011f\u0131ml\u0131l\u0131klar\u0131n Modellenmesi:<\/strong> Yap\u0131n\u0131n par\u00e7alar\u0131 aras\u0131ndaki ili\u015fkileri yakalamak i\u00e7in grafik modellerin kullan\u0131lmas\u0131.<\/li>\n<li><strong>\u00c7\u0131kar\u0131m:<\/strong> Genellikle optimizasyon algoritmalar\u0131 arac\u0131l\u0131\u011f\u0131yla en olas\u0131 \u00e7\u0131kt\u0131 yap\u0131s\u0131n\u0131 bulma.<\/li>\n<li><strong>Verilerden \u00d6\u011frenme:<\/strong> Etiketli \u00f6rneklerden modelin parametrelerini \u00f6\u011frenmek i\u00e7in yap\u0131land\u0131r\u0131lm\u0131\u015f kay\u0131p fonksiyonlar\u0131n\u0131 kullanma.<\/li>\n<\/ol>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahminin Temel \u00d6zelliklerinin Analizi<\/h2>\n<ul>\n<li><strong>Karma\u015f\u0131kl\u0131k Y\u00f6netimi:<\/strong> Karma\u015f\u0131k ili\u015fkileri modelleyebilir.<\/li>\n<li><strong>Genelleme:<\/strong> \u00c7e\u015fitli alanlarda ge\u00e7erlidir.<\/li>\n<li><strong>Y\u00fcksek Boyutluluk:<\/strong> Y\u00fcksek boyutlu \u00e7\u0131kt\u0131 alanlar\u0131n\u0131 i\u015fleyebilir.<\/li>\n<li><strong>Hesaplamal\u0131 Zorluklar:<\/strong> Sorunlar\u0131n karma\u015f\u0131k do\u011fas\u0131ndan dolay\u0131 genellikle hesaplama a\u00e7\u0131s\u0131ndan yo\u011fundur.<\/li>\n<\/ul>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahmin T\u00fcrleri: Tablolar\u0131 ve Listeleri Kullan\u0131n<\/h2>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<th>\u00d6rnek Kullan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Grafiksel Modeller<\/td>\n<td>Grafikleri kullanarak yap\u0131y\u0131 modeller.<\/td>\n<td>Resim etiketleme<\/td>\n<\/tr>\n<tr>\n<td>S\u0131ra Tahmin Modelleri<\/td>\n<td>Etiket dizilerini tahmin eder.<\/td>\n<td>Konu\u015fma tan\u0131ma<\/td>\n<\/tr>\n<tr>\n<td>A\u011fa\u00e7 Tabanl\u0131 Modeller<\/td>\n<td>Yap\u0131y\u0131 bir a\u011fa\u00e7 olarak modeller.<\/td>\n<td>S\u00f6zdizimi ayr\u0131\u015ft\u0131rma<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahmini Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<h3>Kullan\u0131m Alanlar\u0131<\/h3>\n<ul>\n<li><strong>Do\u011fal Dil \u0130\u015fleme:<\/strong> S\u00f6zdizimi ayr\u0131\u015ft\u0131rma, makine \u00e7evirisi.<\/li>\n<li><strong>Bilgisayar g\u00f6r\u00fc\u015f\u00fc:<\/strong> Nesne tan\u0131ma, g\u00f6r\u00fcnt\u00fc b\u00f6l\u00fctleme.<\/li>\n<li><strong>Biyoinformatik:<\/strong> Protein katlanmas\u0131 tahmini.<\/li>\n<\/ul>\n<h3>Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h3>\n<ul>\n<li><strong>A\u015f\u0131r\u0131 uyum g\u00f6sterme:<\/strong> D\u00fczenlile\u015ftirme teknikleri.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik:<\/strong> Verimli \u00e7\u0131kar\u0131m algoritmalar\u0131.<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahmin<\/th>\n<th>s\u0131n\u0131fland\u0131rma<\/th>\n<th>Regresyon<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u00c7\u0131k\u0131\u015f T\u00fcr\u00fc<\/td>\n<td>Yap\u0131land\u0131r\u0131lm\u0131\u015f Nesneler<\/td>\n<td>Ayr\u0131k Etiketler<\/td>\n<td>S\u00fcrekli De\u011ferler<\/td>\n<\/tr>\n<tr>\n<td>Karma\u015f\u0131kl\u0131k<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Il\u0131man<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<tr>\n<td>\u0130li\u015fki Modelleme<\/td>\n<td>A\u00e7\u0131k<\/td>\n<td>\u00d6rt\u00fcl\u00fc<\/td>\n<td>Hi\u00e7biri<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahminle \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<ul>\n<li><strong>Derin \u00d6\u011frenme Entegrasyonu:<\/strong> Daha iyi \u00f6zellik \u00f6\u011frenimi i\u00e7in derin \u00f6\u011frenme y\u00f6ntemlerini birle\u015ftirme.<\/li>\n<li><strong>Ger\u00e7ek Zamanl\u0131 \u0130\u015fleme:<\/strong> Ger\u00e7ek zamanl\u0131 uygulamalar i\u00e7in optimizasyon.<\/li>\n<li><strong>Alanlar Aras\u0131 Transfer \u00d6\u011frenimi:<\/strong> Modellerin farkl\u0131 alanlara uyarlanmas\u0131.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Yap\u0131land\u0131r\u0131lm\u0131\u015f Tahminle Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular\u0131, yap\u0131land\u0131r\u0131lm\u0131\u015f tahminin veri toplama a\u015famas\u0131nda yard\u0131mc\u0131 olabilir. Sa\u011flam ve \u00e7e\u015fitli e\u011fitim setlerinin olu\u015fturulmas\u0131na yard\u0131mc\u0131 olarak, IP tabanl\u0131 k\u0131s\u0131tlamalar olmaks\u0131z\u0131n \u00e7e\u015fitli kaynaklardan yap\u0131land\u0131r\u0131lm\u0131\u015f verilerin b\u00fcy\u00fck \u00f6l\u00e7ekli olarak toplanmas\u0131n\u0131 sa\u011flayabilirler. \u00dcstelik proxy sunucular\u0131n sa\u011flad\u0131\u011f\u0131 h\u0131z ve anonimlik, ger\u00e7ek zamanl\u0131 \u00e7eviri veya i\u00e7erik ki\u015fiselle\u015ftirme gibi ger\u00e7ek zamanl\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin uygulamalar\u0131nda kritik \u00f6neme sahip olabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/repository.upenn.edu\/cgi\/viewcontent.cgi?article=1162&amp;context=cis_papers\" target=\"_new\" rel=\"noopener nofollow\">Ko\u015fullu Rastgele Alanlar: Giri\u015f<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.cornell.edu\/people\/tj\/publications\/joachims_etal_09a.pdf\" target=\"_new\" rel=\"noopener nofollow\">Yap\u0131sal Destek Vekt\u00f6r Makineleri<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy: Proxy Sunucu \u00c7\u00f6z\u00fcmleri<\/a><\/li>\n<\/ul>\n<p>Yukar\u0131daki ba\u011flant\u0131lar, yap\u0131land\u0131r\u0131lm\u0131\u015f tahminle ilgili kavramlar\u0131n, metodolojilerin ve uygulamalar\u0131n daha derinlemesine anla\u015f\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>","protected":false},"featured_media":479181,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479180","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Structured Prediction<\/mark>","faq_items":[{"question":"What is Structured Prediction?","answer":"<p>Structured Prediction is a field in machine learning that deals with predicting structured objects, like sequences, trees, or graphs, rather than simple scalar values. These objects often have complex relationships between their elements, and Structured Prediction models aim to capture these relationships to make predictions.<\/p>"},{"question":"How did Structured Prediction originate?","answer":"<p>Structured Prediction originated in the 1990s, when researchers began focusing on predicting complex structured objects. The development of models like Conditional Random Fields (CRFs) in 2001 was instrumental in defining this field.<\/p>"},{"question":"What are the main types of Structured Prediction?","answer":"<p>The main types of Structured Prediction are Graphical Models that use graphs to model structure, Sequence Prediction Models that predict sequences of labels, and Tree-based Models that model the structure as a tree. Examples include image labeling, speech recognition, and syntax parsing.<\/p>"},{"question":"How does Structured Prediction work?","answer":"<p>Structured Prediction works by representing input data in a feature space, modeling interdependencies using graphical models, finding the most likely output structure through inference algorithms, and learning the model parameters using structured loss functions.<\/p>"},{"question":"What are the key features of Structured Prediction?","answer":"<p>Key features of Structured Prediction include the ability to handle complexity, applicability across various domains, capacity to deal with high-dimensional output spaces, and computational challenges due to the complex nature of problems.<\/p>"},{"question":"What are the current problems and solutions in Structured Prediction?","answer":"<p>Current problems in Structured Prediction include overfitting, which can be addressed using regularization techniques, and scalability, which can be handled with efficient inference algorithms.<\/p>"},{"question":"How can Structured Prediction be used in the future?","answer":"<p>The future of Structured Prediction includes integrating deep learning methods for better feature learning, optimizing for real-time applications, and implementing cross-domain transfer learning.<\/p>"},{"question":"What is the association between Structured Prediction and proxy servers like OneProxy?","answer":"<p>Proxy servers, such as those provided by OneProxy, can assist in the data collection phase of structured prediction by enabling large-scale scraping of data from diverse sources. They also support real-time applications of structured prediction through speed and anonymity.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479180","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\/479180\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/479181"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}