{"id":2769,"date":"2012-01-01T15:15:00","date_gmt":"2012-01-01T14:15:00","guid":{"rendered":"https:\/\/perso.lirmm.fr\/christophe-paul\/?p=2769"},"modified":"2024-04-19T15:20:12","modified_gmt":"2024-04-19T13:20:12","slug":"kernel-2","status":"publish","type":"post","link":"https:\/\/www.lirmm.fr\/christophe-paul\/2012\/01\/01\/kernel-2\/","title":{"rendered":"KERNEL"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\"><strong>KERNEL<\/strong> (Languedoc-Roussillon Regional grant \u201cchercheur d\u2019avenir)<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Massive datas arise from most of the scientific disciplines, including biology, physics, environmental and health science\u2026 To handle the huge amount of (hidden) information represented by these datas require a systematic and rigorous approach ranging from databases and data mining to algorithms. A key to improve the algorithmic efficiency is to simplify, to filter, to reduce the given data sets. Underlying most of the heuristic methods or even the exact algorithms, data-preprocessing strategies have been developed since decades. However, only recently an attempt to formalize the concept of preprocessing algorithm has been proposed through the&nbsp;<em>kernelization theory<\/em>. To date, kernelization algorithms have been mostly designed to tackle NP-hard graph algorithmic problems. The objective of the&nbsp;<em>KERNEL<\/em> project is to expand to domain of application of the kernelization theory to other fields at the interface with computer science.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Dates:<\/strong> 2012-2015<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Project leader:&nbsp;<\/strong>C. Paul (LIRMM, CNRS, Universit\u00e9 Montpellier)&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-font-size is-style-outline has-small-font-size is-style-outline--1\"><a class=\"wp-block-button__link has-black-color has-white-background-color has-text-color has-background has-link-color wp-element-button\" href=\"http:\/\/www.lsis.org\/demograph\/\" style=\"border-radius:18px\" target=\"_blank\" rel=\"noreferrer noopener\">More information<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>KERNEL (Languedoc-Roussillon Regional grant \u201cchercheur d\u2019avenir) Massive datas arise from most of the scientific disciplines, including biology, physics, environmental and health science\u2026 To handle the huge amount of (hidden) information represented by these datas require a systematic and rigorous approach ranging from databases and data mining to algorithms. A key to improve the algorithmic efficiency is to simplify, to filter, to reduce the given data sets. Underlying most of the<\/p>\n","protected":false},"author":35,"featured_media":2673,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","_uag_custom_page_level_css":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-2769","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-researchgrants"],"uagb_featured_image_src":{"full":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL.jpg",640,380,false],"thumbnail":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL-150x150.jpg",150,150,true],"medium":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL-300x178.jpg",300,178,true],"medium_large":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL.jpg",640,380,false],"large":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL.jpg",640,380,false],"1536x1536":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL.jpg",640,380,false],"2048x2048":["https:\/\/www.lirmm.fr\/christophe-paul\/wp-content\/uploads\/sites\/31\/2021\/04\/KERNEL.jpg",640,380,false]},"uagb_author_info":{"display_name":"Perso Admin","author_link":"https:\/\/www.lirmm.fr\/christophe-paul\/author\/lirmmperso\/"},"uagb_comment_info":0,"uagb_excerpt":"KERNEL (Languedoc-Roussillon Regional grant \u201cchercheur d\u2019avenir) Massive datas arise from most of the scientific disciplines, including biology, physics, environmental and health science\u2026 To handle the huge amount of (hidden) information represented by these datas require a systematic and rigorous approach ranging from databases and data mining to algorithms. A key to improve the algorithmic efficiency&hellip;","_links":{"self":[{"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/posts\/2769","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/comments?post=2769"}],"version-history":[{"count":0,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/posts\/2769\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/media\/2673"}],"wp:attachment":[{"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/media?parent=2769"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/categories?post=2769"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.lirmm.fr\/christophe-paul\/wp-json\/wp\/v2\/tags?post=2769"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}