Weight Vector Optimization of the Hinge Loss Function

Weight Vector Optimization of the Hinge Loss Function

For Educational Purposes and better understanding of Weight Vector Optimization and Linear Binary Classification based on the Gradient Descent optimization algorithm

-Weight Optimization of the Hinge Loss Function

Tasks:

-Modify the number of inputs adding f(5,:), f(6,:)..

-Add more features for each input: 3 features: f(1,:)=[1.5 0 6]...

-Change the number of iterations

-Modify the Learning Rate

-Change the initialized values of the weight vector

-Change the Loss Function and the corresponding Gradient