Linear regression aims to fit the parameters based on the training set Tx D = {(x(i),y(i)), i = 1, 2,...,m} so that the hypothesis function he (x) 00+ 01x₁ + 0₂x₂+.. + Onxn can better predict the output y of a new input vector x. Please derive the stochastic gradient descent update rule which can update repeatedly to minimize the least squares cost function J(0). ...... = =

Linear regression aims to fit the parameters based on the training set Tx D = {(x(i),y(i)), i = 1, 2,...,m} so that the hypothesis function he (x) 00+ 01x₁ + 0₂x₂+.. + Onxn can better predict the output y of a new input vector x. Please derive the stochastic gradient descent update rule which can update repeatedly to minimize the least squares cost function J(0). ...... = =

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter11: Nonlinear Programming

Section11.3: Convex And Concave Functions

Problem 23P

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