Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and,
\nmore generally, linear MKL regularised by the Bregman divergence,
\nusing the Sequential Minimal Optimization (SMO) algorithm. The SMO
\nalgorithm is simple, easy to implement and adapt, and efficiently
\nscales to large problems. As a result, it has gained widespread
\nacceptance and SVMs are routinely trained using SMO in diverse real
\nworld applications. Training using SMO has been a long standing goal
\nin MKL for the very same reasons. Unfortunately, the standard MKL dual
\nis not differentiable, and therefore can not be optimised using SMO
\nstyle co-ordinate ascent. In this paper, we demonstrate that linear
\nMKL regularised with the $p$-norm squared, or with certain Bregman
\ndivergences, can indeed be trained using SMO. The resulting algorithm
\nretains both simplicity and efficiency and is significantly faster
\nthan state-of-the-art specialised $p$-norm MKL solvers. We show that
\nwe can train on a hundred thousand kernels in approximately seven
\nminutes and on fifty thousand points in less than half an hour on a
\nsingle core.<\/p>\n","protected":false},"excerpt":{"rendered":"
Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, easy to implement and adapt, and efficiently scales to large problems. As a result, it has gained widespread acceptance and SVMs are routinely […]<\/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-163521","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2010-12-1","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":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/manikvarma.org\/pubs\/vishy10.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"S. V. N. Vishwanathan","user_id":0,"rest_url":false},{"type":"text","value":"Z. Sun","user_id":0,"rest_url":false},{"type":"text","value":"N. Theera-Ampornpunt","user_id":0,"rest_url":false},{"type":"text","value":"M. 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