• For SVM, get rid of 1/m, this is the other convention made for SVM. that is if we get rid m, that the features won't likely to change.See the examples in red for more intuition
  • For the other convention, we simplify the equation with denotes the cost function with A, and regularization term with B.
  • Now we''re modifying, the other convention to be just like the simple term on the right.
  • lambda nullified, instead we are using c term.
  • Is not that c = 1/lambda. But it posses equal result to lambda.
  • The c value is really small so that B can have higher weight than cA. We don't weight B anymore, but instead makes A much more lighter. This way, we still trying to minimize the cost function.

  • These are mathematical definition in SVM
  • Differ from logistic regression, SVM doesn't count probability anymore. Instead we are using either 1 or 0 (not range from 0 to 1 like in logistic) for condition stated above
  • Next, what hypothesis SVM result, dig more about Optimization Objective, and add a little bit to manage more complex non-linear function.