{"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\/fr\/wiki\/structured-prediction\/","title":{"rendered":"Pr\u00e9diction structur\u00e9e"},"content":{"rendered":"<p>La pr\u00e9diction structur\u00e9e fait r\u00e9f\u00e9rence au probl\u00e8me de la pr\u00e9diction d&#039;objets structur\u00e9s, plut\u00f4t que de valeurs scalaires discr\u00e8tes ou r\u00e9elles. Ce domaine de l\u2019apprentissage automatique consiste souvent \u00e0 pr\u00e9dire plusieurs r\u00e9sultats pr\u00e9sentant des interd\u00e9pendances complexes. Il est largement utilis\u00e9 dans divers domaines tels que le traitement du langage naturel, la bioinformatique, la vision par ordinateur, etc. Les mod\u00e8les de pr\u00e9diction structur\u00e9s capturent les relations entre les diff\u00e9rentes parties d&#039;une structure de sortie et les utilisent pour pr\u00e9dire de nouvelles instances.<\/p>\n<h2>L&#039;histoire de l&#039;origine de la pr\u00e9diction structur\u00e9e et sa premi\u00e8re mention<\/h2>\n<p>Les origines de la pr\u00e9diction structur\u00e9e remontent aux premiers travaux sur les statistiques et l\u2019apprentissage automatique. Dans les ann\u00e9es 1990, les chercheurs ont commenc\u00e9 \u00e0 reconna\u00eetre la n\u00e9cessit\u00e9 de pr\u00e9dire des objets structur\u00e9s complexes plut\u00f4t que de simples valeurs scalaires. Cela a conduit au d\u00e9veloppement de mod\u00e8les tels que les champs al\u00e9atoires conditionnels (CRF) par John Lafferty, Andrew McCallum et Fernando Pereira en 2001, qui ont jou\u00e9 un r\u00f4le d\u00e9terminant dans la r\u00e9solution de ces probl\u00e8mes.<\/p>\n<h2>Informations d\u00e9taill\u00e9es sur la pr\u00e9diction structur\u00e9e\u00a0: \u00e9largir le sujet<\/h2>\n<p>La pr\u00e9diction structur\u00e9e implique la pr\u00e9diction d&#039;un objet structur\u00e9 (par exemple, une s\u00e9quence, un arbre ou un graphique) qui a g\u00e9n\u00e9ralement des relations entre ses \u00e9l\u00e9ments. Les principaux composants de la pr\u00e9diction structur\u00e9e comprennent\u00a0:<\/p>\n<h3>Des mod\u00e8les<\/h3>\n<ul>\n<li><strong>Mod\u00e8les graphiques\u00a0:<\/strong> Tels que les CRF, les mod\u00e8les de Markov cach\u00e9s (HMM).<\/li>\n<li><strong>Machines \u00e0 vecteurs de support structur\u00e9s\u00a0:<\/strong> Une g\u00e9n\u00e9ralisation des SVM pour les sorties structur\u00e9es.<\/li>\n<\/ul>\n<h3>Entra\u00eenement<\/h3>\n<ul>\n<li><strong>Fonctions de perte structur\u00e9es\u00a0:<\/strong> M\u00e9thodes pour quantifier la diff\u00e9rence entre les structures pr\u00e9dites et r\u00e9elles.<\/li>\n<li><strong>Algorithmes d&#039;inf\u00e9rence\u00a0:<\/strong> Des techniques telles que la programmation dynamique, la programmation lin\u00e9aire pour trouver la structure de sortie la plus probable.<\/li>\n<\/ul>\n<h2>La structure interne de la pr\u00e9diction structur\u00e9e\u00a0: comment fonctionne la pr\u00e9diction structur\u00e9e<\/h2>\n<p>Le fonctionnement de la pr\u00e9diction structur\u00e9e peut \u00eatre compris \u00e0 travers les \u00e9tapes suivantes\u00a0:<\/p>\n<ol>\n<li><strong>Repr\u00e9sentation d&#039;entr\u00e9e\u00a0:<\/strong> Cartographie des donn\u00e9es brutes dans un espace de fonctionnalit\u00e9s qui met en \u00e9vidence les d\u00e9pendances structurelles.<\/li>\n<li><strong>Interd\u00e9pendances de mod\u00e9lisation\u00a0:<\/strong> Utiliser des mod\u00e8les graphiques pour capturer les relations entre les parties de la structure.<\/li>\n<li><strong>Inf\u00e9rence:<\/strong> Trouver la structure de sortie la plus probable, souvent via des algorithmes d&#039;optimisation.<\/li>\n<li><strong>Apprendre des donn\u00e9es\u00a0:<\/strong> Utiliser des fonctions de perte structur\u00e9es pour apprendre les param\u00e8tres du mod\u00e8le \u00e0 partir d&#039;exemples \u00e9tiquet\u00e9s.<\/li>\n<\/ol>\n<h2>Analyse des principales caract\u00e9ristiques de la pr\u00e9diction structur\u00e9e<\/h2>\n<ul>\n<li><strong>Gestion de la complexit\u00e9\u00a0:<\/strong> Peut mod\u00e9liser des relations complexes.<\/li>\n<li><strong>G\u00e9n\u00e9ralisation:<\/strong> Applicable dans divers domaines.<\/li>\n<li><strong>Haute dimensionnalit\u00e9\u00a0:<\/strong> Capable de g\u00e9rer des espaces de sortie de grande dimension.<\/li>\n<li><strong>D\u00e9fis informatiques\u00a0:<\/strong> Souvent gourmand en calculs en raison de la nature complexe des probl\u00e8mes.<\/li>\n<\/ul>\n<h2>Types de pr\u00e9diction structur\u00e9e\u00a0: utilisez des tableaux et des listes<\/h2>\n<table>\n<thead>\n<tr>\n<th>Taper<\/th>\n<th>Description<\/th>\n<th>Exemple d&#039;utilisation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mod\u00e8les graphiques<\/td>\n<td>Mod\u00e9lise la structure \u00e0 l\u2019aide de graphiques.<\/td>\n<td>\u00c9tiquetage des images<\/td>\n<\/tr>\n<tr>\n<td>Mod\u00e8les de pr\u00e9diction de s\u00e9quence<\/td>\n<td>Pr\u00e9dit les s\u00e9quences d&#039;\u00e9tiquettes.<\/td>\n<td>Reconnaissance de la parole<\/td>\n<\/tr>\n<tr>\n<td>Mod\u00e8les bas\u00e9s sur des arbres<\/td>\n<td>Mod\u00e9lise la structure sous forme d&#039;arbre.<\/td>\n<td>Analyse syntaxique<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Fa\u00e7ons d&#039;utiliser la pr\u00e9vision structur\u00e9e, les probl\u00e8mes et leurs solutions<\/h2>\n<h3>Les usages<\/h3>\n<ul>\n<li><strong>Traitement du langage naturel\u00a0:<\/strong> Analyse syntaxique, traduction automatique.<\/li>\n<li><strong>Vision par ordinateur:<\/strong> Reconnaissance d&#039;objets, segmentation d&#039;images.<\/li>\n<li><strong>Bioinformatique\u00a0:<\/strong> Pr\u00e9diction du repliement des prot\u00e9ines.<\/li>\n<\/ul>\n<h3>Probl\u00e8mes et solutions<\/h3>\n<ul>\n<li><strong>Surapprentissage\u00a0:<\/strong> Techniques de r\u00e9gularisation.<\/li>\n<li><strong>\u00c9volutivit\u00e9\u00a0:<\/strong> Algorithmes d&#039;inf\u00e9rence efficaces.<\/li>\n<\/ul>\n<h2>Principales caract\u00e9ristiques et autres comparaisons avec des termes similaires<\/h2>\n<table>\n<thead>\n<tr>\n<th>Caract\u00e9ristique<\/th>\n<th>Pr\u00e9diction structur\u00e9e<\/th>\n<th>Classification<\/th>\n<th>R\u00e9gression<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Le type de sortie<\/td>\n<td>Objets structur\u00e9s<\/td>\n<td>\u00c9tiquettes discr\u00e8tes<\/td>\n<td>Valeurs continues<\/td>\n<\/tr>\n<tr>\n<td>Complexit\u00e9<\/td>\n<td>Haut<\/td>\n<td>Mod\u00e9r\u00e9<\/td>\n<td>Faible<\/td>\n<\/tr>\n<tr>\n<td>Mod\u00e9lisation des relations<\/td>\n<td>Explicite<\/td>\n<td>Implicite<\/td>\n<td>Aucun<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspectives et technologies du futur li\u00e9es \u00e0 la pr\u00e9diction structur\u00e9e<\/h2>\n<ul>\n<li><strong>Int\u00e9gration du Deep Learning\u00a0:<\/strong> Int\u00e9gration de m\u00e9thodes d&#039;apprentissage en profondeur pour un meilleur apprentissage des fonctionnalit\u00e9s.<\/li>\n<li><strong>Traitement en temps r\u00e9el\u00a0:<\/strong> Optimisation pour les applications temps r\u00e9el.<\/li>\n<li><strong>Apprentissage par transfert inter-domaines\u00a0:<\/strong> Adapter les mod\u00e8les dans diff\u00e9rents domaines.<\/li>\n<\/ul>\n<h2>Comment les serveurs proxy peuvent \u00eatre utilis\u00e9s ou associ\u00e9s \u00e0 une pr\u00e9diction structur\u00e9e<\/h2>\n<p>Les serveurs proxy, comme ceux fournis par OneProxy, peuvent aider dans la phase de collecte de donn\u00e9es de pr\u00e9diction structur\u00e9e. Ils peuvent permettre la r\u00e9cup\u00e9ration \u00e0 grande \u00e9chelle de donn\u00e9es structur\u00e9es provenant de diverses sources sans restrictions bas\u00e9es sur la propri\u00e9t\u00e9 intellectuelle, contribuant ainsi \u00e0 la cr\u00e9ation d&#039;ensembles de formation robustes et diversifi\u00e9s. De plus, la vitesse et l&#039;anonymat fournis par les serveurs proxy peuvent \u00eatre critiques dans les applications en temps r\u00e9el de pr\u00e9diction structur\u00e9e, comme la traduction en temps r\u00e9el ou la personnalisation de contenu.<\/p>\n<h2>Liens connexes<\/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\">Champs al\u00e9atoires conditionnels\u00a0: une introduction<\/a><\/li>\n<li><a href=\"https:\/\/www.cs.cornell.edu\/people\/tj\/publications\/joachims_etal_09a.pdf\" target=\"_new\" rel=\"noopener nofollow\">Machines vectorielles de support structurel<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/fr\/\" target=\"_new\" rel=\"noopener\">OneProxy\u00a0: solutions de serveur proxy<\/a><\/li>\n<\/ul>\n<p>Les liens ci-dessus permettent une compr\u00e9hension plus approfondie des concepts, m\u00e9thodologies et applications li\u00e9s \u00e0 la pr\u00e9diction structur\u00e9e.<\/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\/fr\/wp-json\/wp\/v2\/wiki\/479180","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/479180\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media\/479181"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media?parent=479180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}