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Let us suppose we know that target variable can only be positive and decide to learn prediction function of type ????(). Remember ???? ? = ? if ? ≥ 0 and ???? ? = 0 if ? < 0. Show that MSE of this predictor is not a quadratic form. We decided to use gradient descent algorithm to find w that minimizes MSE of this predictor. Derive the update formula in a vector form. EXTRA CREDIT: Show that MSE for this predictor has the global minimum (i.e, show that MSE is a convex function)

Let us suppose we know that target variable can only be positive and decide to learn prediction function of type ????(). Remember ???? ? = ? if ? ≥ 0 and ???? ? = 0 if ? < 0. Show that MSE of this predictor is not a quadratic form. We decided to use gradient descent algorithm to find w that minimizes MSE of this predictor. Derive the update formula in a vector form. EXTRA CREDIT: Show that MSE for this predictor has the global minimum (i.e, show that MSE is a convex function)

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Let us suppose we know that target variable can only be positive and decide to learn prediction function of type ????(). Remember ???? ? = ? if ? ≥ 0 and ???? ? = 0 if ? < 0. Show that MSE of this predictor is not a quadratic form. We decided to use gradient descent algorithm to find w that minimizes MSE of this predictor. Derive the update formula in a vector form. EXTRA CREDIT: Show that MSE for this predictor has the global minimum (i.e, show that MSE is a convex function) 

 

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