La 23 od picm uy rnopx dt y12 aa loe0oe0 ns120 ns120

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Unformatted text preview: ine algorithm 1. Solve the quadratic programming problem: such that and 1. Use Matlab's quadprog to find the optimal 2. Find 3. Find by choosing a support vector (a point with ) and solving Example in Matlab The following code, taken verbatim from the lecture, shows how to use Matlab built- in SVM routines (found in the Bioinformatics toolkit) to do classification through support vector machines. la 23 od _; [,]=picm('; UY rnopX) dt =Y:12; aa (,:) l=[oe(,0)oe(,0); -ns120 ns120] [ri,et =cosaid'odu'40; tants] rsvln(hlOt,0) %Gvsidcso tanadts;s,tani amti o 0o 1 1weetepitsol b ue a pr o tetann st ie nie f ri n et o ri s arx f r, hr h on hud e sd s at f h riig e smtut=smri(aatan:,ltan,'hwlt,tu) vSrc vtandt(ri,) (ri) soPo' re; wikicour Stat841&pr intable= yes 65/74 10/09/2013 Stat841 - Wiki Cour se Notes The plot produced by training on some of the 2_ 3 data's first two features. y =smlsiysmtut dt(et:,'hwlt,tu) h vcasf(vSrc, aats,) soPo' re; The plot produced by testing some of the 2_ 3 data. SVM in Gene Selection DNA micro- arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new micro- array devices generate bewildering amounts of raw data, new analytical methods must be developed to sort out whether cancer tissues have distinctive signatures of gene expression over normal tissues or other types of cancer tissues. Exte ntion:Support Ve ctor M achine s for Patte rn Re cognition [32] ( us/um/people/cburges/papers/svmtutorial.pdf) This paper talks about linear Support Vector Machines for separable and nonseparable data by working through a non- trivial example in detail, and also it describes a mechanical analog and when SVM solutions are unique and when they are global. From this paper we can know support vector training can be practically implemented, and the kernel mapping techniq...
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