give the steps by steps answer 2. Given a two-category classification problem under the univariate case, wherethere are two training sets (one for each category) as follows:D₁ = (-3,-1,0,4}D₂ = {-2,1,2,3,6,8}Given the test example x = 5, please answer the following questions:haveanda) Assume that the likelihood function of each category has certain paramétricform. Specifically, wep(x | w₁) N, 07)p(x₂)~ N(μ₂, 02). Which category should we decide on whenmaximum-likelihood estimation is employed to make the prediction?b) Following the above assumption, suppose we further know that o = 2, 02= 1, ₁ ~ N(0, 1) and 2~ N(2, 1). Which category should we decideon t when Bayesian estimation is employed to make the prediction?c) Assume that the likelihood function of each category doesn't have anyparametric form. Furthermore, the window function for either category takesthe form of N(0, 1), while the window width for ₁ and is 2 and 5respectively. Which category should we decide on x when Parzen windowsis employed to make the prediction?

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Answered: Given a two-category classification…

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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give the steps by steps answer

2. Given a two-category classification problem under the univariate case, where
there are two training sets (one for each category) as follows:
D₁ = (-3,-1,0,4}
D₂ = {-2,1,2,3,6,8}
Given the test example x = 5, please answer the following questions:
have
and
a) Assume that the likelihood function of each category has certain paramétric
form. Specifically, we
p(x | w₁) N, 07)
p(x₂)~ N(μ₂, 02). Which category should we decide on when
maximum-likelihood estimation is employed to make the prediction?
b) Following the above assumption, suppose we further know that o = 2,

02= 1, ₁ ~ N(0, 1) and 2~ N(2, 1). Which category should we decide
on t when Bayesian estimation is employed to make the prediction?
c) Assume that the likelihood function of each category doesn't have any
parametric form. Furthermore, the window function for either category takes
the form of N(0, 1), while the window width for ₁ and is 2 and 5
respectively. Which category should we decide on x when Parzen windows
is employed to make the prediction?

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Given a two-category classification problem under the univariate case, where there are two training sets (one for each category) as follows: D₁ = (-3,-1,0,4} D₂ = {-2,1,2,3,6,8} Given the test example x = 5, please answer the following questions: have and a) Assume that the likelihood function of each category has certain paramétric form. Specifically, we p(x | w₁) N, 07) p(x₂)~ N(μ₂, 02). Which category should we decide on when maximum-likelihood estimation is employed to make the prediction?

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