{"id":469,"date":"2013-02-20T12:50:42","date_gmt":"2013-02-20T03:50:42","guid":{"rendered":"http:\/\/nagasakilab.csml.org\/en\/?p=469"},"modified":"2013-02-20T12:50:42","modified_gmt":"2013-02-20T03:50:42","slug":"the-19th-in-silico-megabank-research-seminar%ef%bc%88december-14-2012%ef%bc%89","status":"publish","type":"post","link":"https:\/\/nagasakilab.csml.org\/en\/archives\/469","title":{"rendered":"The 19th In Silico Megabank Research Seminar\uff08December 14, 2012\uff09"},"content":{"rendered":"

The 19th In Silico Megabank Research Seminar will be held on Friday, December 14.<\/p>\n

\u30fbDate\/Time: December 14(Fri.) 17:00\u2010
\n\u30fbVenue: Seminar Room 303 (3F), Research Building No.3, Electrical Engineering and plied Physics
\nhttp:\/\/www.megabank.tohoku.ac.jp\/english\/info\/access.html<\/a>
\n\u30fbTitle: Robust Sparse Regression Modeling
\n\u30fbLecturer: Heewon Park (Chuo University)
\n\u30fbAbstract:
\nThe L1 type of the regularization technique, which has been attracting attention recently, has the advantage in that presumption and the variable selection can be performed simultaneously by adding a L1 type of penalty to the likelihood function, and it is especially effective to analyze high-dimensional data including the genomic data. In this study, I introduce an effective algorithm for the analysis of the genomic data and propose a variety of robust sparse regression modeling techniques based on the L1 type of the regularization technique that is more effective than any existing research. As a result of verifying the proposal technique utilizing a numerical experiment, the effectiveness of it has become clear compared with existing techniques.<\/p>\n

\u30fbOrganizer: Masao Nagasaki<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

The 19th In Silico Megabank Research Seminar will be held on Friday, December 14. \u30fbDate\/Time: December 14(Fri.) 17:00\u2010 \u30fbVenue: Seminar Room 303 (3F), Research Building No.3, Electrical Engineering and plied Physics http:\/\/www.megabank.tohoku.ac.jp\/english\/info\/access.html \u30fbTitle: Robust Sparse Regression Modeling \u30fbLecturer: Heewon Park … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"passster_activate_protection":false,"passster_protect_child_pages":"","passster_protection_type":"password","passster_password":"","passster_activate_overwrite_defaults":"","passster_headline":"","passster_instruction":"","passster_placeholder":"","passster_button":"","passster_id":"","passster_activate_misc_settings":"","passster_redirect_url":"","passster_hide":"no","passster_area_shortcode":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-469","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/posts\/469","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/comments?post=469"}],"version-history":[{"count":2,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/posts\/469\/revisions"}],"predecessor-version":[{"id":471,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/posts\/469\/revisions\/471"}],"wp:attachment":[{"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/media?parent=469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/categories?post=469"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nagasakilab.csml.org\/en\/wp-json\/wp\/v2\/tags?post=469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}