Formulate a variation of regularized least-squares classification in which L1-loss is used instead of L2-loss.


1.       Show that the effect of the bias term can be accounted for by adding a constant amount to each entry of the n × n kernel similarity matrix when using kernels with linear models.

2.       Formulate a variation of regularized least-squares classification in which L1-loss is used instead of L2-loss. How would you expect each of these methods to behave in the presence of outliers? Which of these methods is more similar to SVMs with hinge loss? Discuss the challenges of using gradient-descent with this problem as compared to the regularized least-squares formulation.

find the cost of your paper

The post Formulate a variation of regularized least-squares classification in which L1-loss is used instead of L2-loss. appeared first on Best Custom Essay Writing Services | EssayBureau.com.

[Button id=”1″]

Thanks for installing the Bottom of every post plugin by Corey Salzano. Contact me if you need custom WordPress plugins or website design.

Looking for a Similar Assignment? Our ENL Writers can help. Get your first order at 15% off!

Order

Hi there! Click one of our representatives below and we will get back to you as soon as possible.

Chat with us on WhatsApp
%d bloggers like this: