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dc.contributor.authorVishwanathan, S. V. N.en_US
dc.date.accessioned2011-04-27T20:14:25Z
dc.date.available2011-04-27T20:14:25Z
dc.date.issued2011-04-15
dc.identifier.urihttp://hdl.handle.net/1853/38699
dc.descriptionS V N Vishwanathan of Purdue University presented a lecture on April 15, 2011 from 2:00 pm - 3:00 pm in room 1447 of the Klaus Advanced Computing Building on the Georgia Tech campus.en_US
dc.descriptionRuntime: 57:10 minutesen_US
dc.description.abstractOur 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 trained using SMO in diverse real world applications. Training using SMO has been a long standing goal in MKL for the very same reasons. Unfortunately, the standard MKL dual is not differentiable, and therefore can not be optimised using SMO style co-ordinate ascent. In this paper, we demonstrate that linear MKL regularised with the $p$-norm squared, or with certain Bregman divergences, can indeed be trained using SMO. The resulting algorithm retains both simplicity and efficiency and is significantly faster than the state-of-the-art specialised $p$-norm MKL solvers. We show that we can train on a hundred thousand kernels in less than fifteen minutes and on fifty thousand points in nearly an hour on a single core using standard hardware.en_US
dc.format.extent57:10 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesComputational Science and Engineering Seminar Seriesen_US
dc.subjectMachine learningen_US
dc.subjectOptimizationen_US
dc.titleOptimization for Machine Learning: SMO-MKL and Smoothing Strategiesen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenamePurdue Universityen_US


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