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: =) Assume that the likelihood function of each category has certain parametric form. Specifically, we and p(x|w₂) have p(x|w₁) N(₁7) N(2, 2). Which category should we decide on when maximum-likelihood estimation is employed to make the prediction? ) Following the abou

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
icon
Related questions
Question

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,
Transcribed Image Text: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?
Transcribed Image Text: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?
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 5 steps with 17 images

Blurred answer
Follow-up Questions
Read through expert solutions to related follow-up questions below.
Follow-up Question

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? 

Solution
Bartleby Expert
SEE SOLUTION
Knowledge Booster
Bellman operator
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.
Recommended textbooks for you
Database System Concepts
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
C How to Program (8th Edition)
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
Database Systems: Design, Implementation, & Manag…
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
Programmable Logic Controllers
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education