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ECSO74
Following Paper ID and Roll No. to be filled in yourAnswer
.
B.Tech.
(SEM. VII) ODD SEMESTER THEORY
EXAMINATION 2013-14 PATTERN RECOGNITION 'Iime : 3 Hours
Note
l.
: (1) (2)
Total Marlcs
100
AttemptAllquestions. Make suitable assumption if required.
Attempt any TWO Parts
(a)
:
:-
What is Pattern Recognition ? Explain the difference between statistical and structural approaches to pattern recognition.
(b) (i)
What is Pattern classification ? What are major paradigms of Machine Learning ?
(ii)
Explain Learning and Adaptation. What are the components of a learning system
(c) (i) (ii)
?
What do you mean by mean and covariance What are random variables
?
?
Explain chi-square test.
i
2.
Attempt any TWO parts
(a)
:-
What is Bayes'theorem ? Explain. Also discuss Bayes' classifier using some example in detail.
ECSOZIDNG-52080
[Turn Over
r:(b) (D
Consider the Bayesian classifier for the uniformly distributed classes, where
rl | r(>{,) : lu,
, X€lat,azl
;u, ,
,l
I;-- o, , P(x/wr) =
muullion
X€[br,bz]
10r; \,
.
:
muullion
Show the classification results for some values for a and b.
(ii)
("muullion" means "otherwise").
Consider the classifier, where the risk is taken into account as follows
Lrr: Lrr= 1 ja
:
?r.rr:
?"rr:2
construct the classifiet ("ia" means "and").
(c) ' What is discriminant function a formula
of conditional risk
:l'
R 1o,l
? Discuss
it in detail using
:
n
,():
r(cti
jI
lwj) P(wj I x)
derive the formula for the likelihood ratio.
3.
Attempt any TWO Parts
(a)
:-
Write a short note on Hidden Markov Model (HMM).
ECS074/pNG-s2080
(b)
(c)
Write short notes on the following
:-
(i)
Gaussian mixtuie models
(iD
Fisher linear discriminant analysis'
Show that in the likelihood estimation (ML) the sample
mean is'equal to the rnean
of samples' Consider that
S:Vi. 4.
Attempt anY TWO Parts
:-
(a)WriteanalgorithmforK-Nearestneighborestimation. Explain.
(b)WhyuseFvqclasses?WhatistheFuzzyclassification process ?
(c)
5.
Write short notes on the following
(i)
Petzenwindows
(iD
DensitYEstimation.
AttemPt anY TWO Parts
(a) (i)
:-
:-
Four samples are to be clustered into three clusters' Show all possible sets of clusters' How many sets there are ?
(ii)
What do you mean by cluster validation ?
(b) What do you mean by supervised learning and unsupervised learning
? Explain' Discuss any
unsupervised learning algorithm with some example'
(c)
Write short notes on the following
(i) (ii)
:-
K-Means PartitionalAlgorithrn
HierarchicalClustering'
ECS074/pNG-s28E0
11000
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