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Description: SVMLSPex02.m
Two Dimension SVM Problem, Two Class and Separable Situation
Difference with SVMLSPex01.m:
Take the Largrange Function (16)as object function insteads ||W||,
so it need more time than SVMLSex01.m
Method from Christopher J. C. Burges:
"A Tutorial on Support Vector Machines for Pattern Recognition", page 9
Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16)
Subject to: sum{ai*yi}=0 ,function (15)
and ai>=0 for any i, the particular set of constraints C2 (page 9, line14).
The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples.
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