Support Vector Machines non probabilistic binary linear classifier

There is a decision boundary

When y=0 and Theta dot x <= -1, cost is 0
When y=1 and Theta dot x >= 1, cost is 0

Best line is decision boundary that maximizes distance to the positive and maximize distance to negative


Nomral of the decision boundary maximies lengths of p means it is hitting source of light the most effectively

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