ASSume WT hawt Fhree classes, aneh Huo Adve nsionnk Baitinghoot Ah Follow ney means and Covacianet makaies :A mela, My = I! ; My = 15]p h I~)2-2 ne [0] ;poy Probabiliticy, J? (»)) = p (

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ASSume WT hawt Fhree classes, aneh Huo Adve nsionnk Baiting hoot Ah Follow ney means and Covacianet makaies : A mela, My = I! ; My = 15] p h I~) 2-2 ne [0] ; poy Probabiliticy, J? (»)) = p (<2) z="Are" j="" i?="" (ws)="" z="0" }="" fname="" als="" lrd="" mand="" fanctim¢="" 0),(%),9="" &)="" ,="" (x)="" ,="" hen="" paph="" +e="" decision="" bowndacy="" rig="" (ens="" -="" 2-="" ¢="" loss="" fy="" a="" fedurt="" veet="" rely="" wy="" z="" wy)="PR" =="" l="" &="" 2="" pled="" aha="" deciscan="" bowndary="" assumia="" pon="y!" d="" ¥y="" 3)="" 2="" -="" re="" peat="" pact="" o="" a="" ssa="" mey="" fl="" covariant="" mates="">< ack ferent : ~*% a i) 3 \ % rh 2 f / \ 5 2 z i} i. find 90 a0, 900 asuming p(a)=p@) = 5, f233 jas ha gecime besedee) s bu 5002 9,00 alr = a, e lass fo a de don yor re (5) . 9 ack="" ferent="" :="" ~*%="" a="" i)="" 3="" \="" %="" rh="" 2="" f="" \="" 5="" 2="" z="" i}="" i.="" find="" 90="" a0,="" 900="" asuming="" p(a)="P@)" =="" 5,="" f233="" jas="" ha="" gecime="" besedee)="" s="" bu="" 5002="" 9,00="" alr="A," e="" lass="" fo="" a="" de="" don="" yor="" re="" (5)="" .="">

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Answered Same DayOct 26, 2022

Answer To: ASSume WT hawt Fhree classes, aneh Huo Adve nsionnk Baitinghoot Ah Follow ney means and Covacianet...

Rhea answered on Oct 27 2022

60 Votes

Given, we have three classes, each 2d Gaussian having the following means and covariance matrices-
and
Prior probabilities:
Let,
We need to-
1. Find linear discriminant functions and then graph the decision boundary regions.
The linear discriminant function for each class is-
Since, , so the covariance is class-independent and hence we can drop the terms which do not contain mean or prior probabilities as irrelevant.
Similarly, we can find and . Solving for separately for and we’ll get the decision boundary.
Using Matlab, we obtain the following-
mu1=[1;3];
mu2=[-1;6];
mu3=[-2;5];
sigma1=[1,-1;-1,4];
sigma2=[1,-1;-1,4];
sigma3=[1,-1;-1,4];
p1=1/5;
p2=1/5;
p3=3/5;
syms x1 x2;
g1 = -0.5*([x1;x2]-mu1)'*[inv(sigma1)*([x1;x2]-mu1)]-log(2*pi)-0.5*log(det(sigma1))+log(p1)
g2 =...

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ASSume WT hawt Fhree classes, aneh Huo Adve nsionnk Baitinghoot Ah Follow ney means and Covacianet makaies :A mela, My = I! ; My = 15]p h I~)2-2 ne [0] ;poy Probabiliticy, J? (»)) = p (

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