Logistic regression aims to train the parameters from the training set D = {(x(i),y(i)), i 1, 2,..., m, y € {0,1}} so that the hypothesis function h(x) = g(¹ x) = (here g(z) is the logistic or sigmod function g(z) : = ) can predict the probability of a 1 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0₁ = 0; + α(y(¹) — hq (x(i)))x;") -

Logistic regression aims to train the parameters from the training set D = {(x(i),y(i)), i 1, 2,..., m, y € {0,1}} so that the hypothesis function h(x) = g(¹ x) = (here g(z) is the logistic or sigmod function g(z) : = ) can predict the probability of a 1 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0₁ = 0; + α(y(¹) — hq (x(i)))x;") -

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter17: Markov Chains

Section17.6: Absorbing Chains

Problem 7P

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