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Different algorithms use different cost functions and optimization techniques. For example, SVMs use a hinge loss function and quadratic programming to find the optimal 'w' and 'b'. Logistic regression uses a sigmoid function and gradient descent. Neural networks can also learn hyperplanes (or more complex decision boundaries) through backpropagation.
