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;") -

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

Problem 1PE

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