Discuss the effect on the bias and variance by making the following changes to a classification algorithm

1.       Suppose that you perform least-squares regression without regularization with the loss function n i=1(yi − W · Xi)2, but you add spherical Gaussian noise with variance λ to each feature. Show that the expected loss with the perturbed features provides a loss function that is identical to that of L2-regularization. Use this result to provide an intuitive explanation of the connection between regularization and noise addition.

2.       Show how to use the representer theorem to derive the closed-form solution of kernel least-squares regression.

3.       Discuss the effect on the bias and variance by making the following changes to a classification algorithm: (a) Increasing the regularization parameter in a support vector machine, (b) Increasing the Laplacian smoothing parameter in a n¨ıve Bayes classifier, (c) Increasing the depth of a decision tree, (d) Increasing the number of antecedents in a rule, (e) Reducing the bandwidth σ, when using the Gaussian kernel in conjunction with a support vector machine.

find the cost of your paper

Suggest a modification of the binary search algorithm that emulates this strategy for a list of names.

1. Suppose that a list contains the values 20 44 48 55 62 66 74 88 93 99 at index positions 0 through 9. Trace the values of the variables….

Explain why insertion sort works well on partially sorted lists.

1. Which configuration of data in a list causes the smallest number of exchanges in a selection sort? Which configuration of data causes the largest number of exchanges? 2. Explain….

Draw a class diagram that shows the relationships among the classes in this new version of the system

Jack decides to rework the banking system, which already includes the classes BankView, Bank, SavingsAccount, and RestrictedSavingsAccount. He wants to add another class for checking accounts. He sees that savings….