4. What are the differences between L2 and L1 regularization? For a given linear decision (w,b), calculate the value of the L2 and L1 penalty 5. Describe majorization-minimization and gradient descent

4. What are the differences between L2 and L1 regularization? For a given linear decision (w,b), calculate the value of the L2 and L1 penalty 5. Describe majorization-minimization and gradient descent

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter14: Game Theory

Section: Chapter Questions

Problem 7RP

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Concept explainers

Decision Tree

A decision tree is a unique kind of probability tree. A popular and powerful tool used for prediction and classification. The decision tree structure is the same as a flowchart with a tree structure. The internal node refers to the attribute test, the branch refers to the outcome of the test, and the leaf node(terminal) contains the class label.

Question

4. What are the differences between L2 and L1 regularization? For a given linear decision (w,b),

calculate the value of the L2 and L1 penalty

5. Describe majorization-minimization and gradient descent

6. Describe the proximal gradient descent method: what is the gradient step? What is the proximal

step? For a set Q={x : ||x||<=1} in 2D space, what is the result of ProxQ(y) where

a) y=[0.5,0.2]; b) y=[2,0]

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