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I am trying to build a SVM from scrath and I would like to maximize this Lagrarian expression: enter image description here

I know what variables means but I would like to know how this maximization is implemeted. Should I start by an alpha close to 0 and increase it until I found support vectors?

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If you consider the Lagrarian expression acting as your loss function for svm. Then use optimization ml techniques to find the best parameter.

Here your alpha is the parameter. So, find the best value of this parameter at which the Lagrarian expression is maximum i.e. maxima of the optimization function, which can be found using argmax function.

But, can I get to know why you are using this expression, since a svm model is generally get trained on the hinge loss function, & this expression has to do nothing without it.

But, if you want the code, then reply me

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