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Theoretical derivation of support vector machine
2022-07-06 12:29:00 【「 25' h 」】
Theoretical derivation of support vector machine
Linear separable support vector machine
General process
Normalization of distance formula and introduction of extreme points
Minimization of Lagrangian dual function
KKT Conditional conclusion
SMO Algorithmic solution L Function maximization α*
Thank you, Mr. Dahai KKT deduction
- Similar to the idea of gradient descent, the optimal value is obtained through the idea of iteration , But there are only restrictions , Solve by moving two variables , More efficient .
Linear support vector machines
The relaxation factor is introduced and transformed into Lagrange function
KKT Conditions introduce relations and find W*
adopt KKT The relationship between b*
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