{"id":151241,"date":"1999-01-01T00:00:00","date_gmt":"1999-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/large-margin-dags-for-multiclass-classification\/"},"modified":"2018-10-16T20:24:05","modified_gmt":"2018-10-17T03:24:05","slug":"large-margin-dags-for-multiclass-classification","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/large-margin-dags-for-multiclass-classification\/","title":{"rendered":"Large Margin DAG&#8217;s for Multiclass Classification"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two\u00adclass classifiers into a multiclass classifier. For an N\u00adclass problem, the DDAG contains N(N-1)\/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel\u00adinduced feature space and uses two\u00adclass maximal margin hyperplanes at each decision\u00adnode of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two\u00adclass classifiers into a multiclass classifier. For an N\u00adclass problem, the DDAG contains N(N-1)\/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"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-151241","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proc. Advances in Neural Information Processing Systems 12","msr_affiliation":"","msr_published_date":"1999-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","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":"211264","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"dagsvm.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/dagsvm.pdf","id":211264,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":211264,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/dagsvm.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"jplatt","user_id":32416,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jplatt"},{"type":"text","value":"Nello Cristianini","user_id":0,"rest_url":false},{"type":"text","value":"John Shawe-Taylor","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169805],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169805,"post_title":"Support Vector Machines","post_name":"support-vector-machines","post_type":"msr-project","post_date":"2001-11-05 12:17:42","post_modified":"2019-08-14 14:33:07","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/support-vector-machines\/","post_excerpt":"Support vector machines are a set of algorithms that learn from data by creating models that maximize their margin of error. Support vector machines\u00a0(SVMs) are a family of algorithms for\u00a0classification,\u00a0regression,\u00a0transduction, novelty detection, and\u00a0semi-supervised learning. They work by choosing a model that\u00a0maximizes the error margin of a training set. SVMs\u00a0were originally developed by\u00a0Vladimir Vapnik in 1963. Since the mid-90s, a energetic research community has grown around them. 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