{"id":163310,"date":"2011-12-16T00:00:00","date_gmt":"2011-12-16T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/parallelizing-the-training-of-the-kinect-body-parts-labeling-algorithm\/"},"modified":"2018-10-16T21:56:46","modified_gmt":"2018-10-17T04:56:46","slug":"parallelizing-the-training-of-the-kinect-body-parts-labeling-algorithm","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/parallelizing-the-training-of-the-kinect-body-parts-labeling-algorithm\/","title":{"rendered":"Parallelizing the Training of the Kinect Body Parts Labeling Algorithm"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present the parallelized implementation of decision forest training as used in Kinect to train the body parts classification system. We describe the practical details of dealing with large training sets and deep trees, and describe how to parallelize over multiple dimensions of the problem.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the parallelized implementation of decision forest training as used in Kinect to train the body parts classification system. We describe the practical details of dealing with large training sets and deep trees, and describe how to parallelize over multiple dimensions of the problem.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-163310","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"BigLearn Workshop at NIPS","msr_affiliation":"","msr_published_date":"2011-12-16","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"BigLearn Workshop at NIPS","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"206257","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"top.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/top.pdf","id":206257,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":206257,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/top.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"mbudiu","user_id":32853,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mbudiu"},{"type":"user_nicename","value":"jamiesho","user_id":32162,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jamiesho"},{"type":"user_nicename","value":"derekmur","user_id":31611,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=derekmur"},{"type":"text","value":"Mark Finocchio","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169537],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169537,"post_title":"DryadLINQ","post_name":"dryadlinq","post_type":"msr-project","post_date":"2007-05-31 11:04:14","post_modified":"2017-06-08 12:01:42","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/dryadlinq\/","post_excerpt":"DryadLINQ is a simple, powerful, and elegant programming environment for writing large-scale data parallel applications running on large PC clusters. 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