Consider svm-dual,i.e., \begin{align} &\text{maximize} \sum_{i=1}^n \alpha_i-\frac{1}{2\lambda} \sum_{i,j=1}^n \alpha_i \alpha_j y_i y_j K(x_i,x_j)\cr &\text{subject to, } 0\leq \alpha_i \leq 1 \end{align} where $K$ is the kernel matrix and $\lambda$ is the regualrization parameter.

I know that $\alpha_i$ is the share of $x_i$ in determining the hyperplane. My question is how can we constrain this share in primal problem? Put it another way, How can we constrain lagrange multipliers(i.e. dual variables) by adding constraints in primal problem?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.