{"id":151995,"date":"2006-05-01T00:00:00","date_gmt":"2006-05-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/training-algorithms-for-hidden-conditional-random-fields\/"},"modified":"2018-10-16T19:58:54","modified_gmt":"2018-10-17T02:58:54","slug":"training-algorithms-for-hidden-conditional-random-fields","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/training-algorithms-for-hidden-conditional-random-fields\/","title":{"rendered":"Training algorithms for hidden conditional random fields"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We investigate algorithms for training hidden conditional random fields (HCRFs) \u2013 a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give experimental results on the TIMIT phone classification task showing how these training options interact, comparing HCRFs to HMMs trained using extended Baum-Welch as well as stochastic gradient methods.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We investigate algorithms for training hidden conditional random fields (HCRFs) \u2013 a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give [&hellip;]<\/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":"","msr-author-ordering":[{"type":"user_nicename","value":"milindm"},{"type":"user_nicename","value":"aselag"},{"type":"user_nicename","value":"alexac"}],"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"International Conference on Acoustics, Speech, and Signal Processing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2004 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"International Conference on Acoustics, Speech, and Signal 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