MCASemester 42024-25Pattern RecognitionKCA033

Pattern Recognition (KCA033) - AKTU Question Paper 2024-25

MCA · Semester 4 · Free PDF Download

This is the official AKTU Pattern Recognition Previous Year Question Paper for MCA Semester 4, academic session 2024-25. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:MCA

Semester:Semester 4

Session:2024-25

University:AKTU / UPTU

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Questions Asked in 2024-25

Pattern Recognition (KCA033) — complete question paper · 100 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 10 = 20

a
Define pattern recognition and give a real-world example
b
What is the role of the covarian ce matrix in multivariate analysis?
c
State Bayes' theorem and its s ignificance in pattern classification
d
What are discriminant functi ons in statistical pattern recognition?
e
What is Maximum Likeli hood Estimation (MLE)?
f
Explain the significance of Princ ipal Component Analysis (PCA) i n pattern recognition
g
Define Parzen window density estimation
h
What is the role of K-Nearest N eighbour (K-NN) in pattern recognition? i. Briefly state any two clusteri ng techniques used in pattern recognition. j. Explain the purpose of cluster v alidation in unsupervised learning

Section BAttempt any three of the following: 10 x 3 = 30

a
Describe the basic design p rinciples of a pattern recognition system
b
Define Bayesian decision theor y and explain its basic decision rules
c
What is Bayesian parameter estimation? Describe its basic pr inciple
d
What is density estimation in pattern recognition? Explain its types
e
Define clustering and explain its significance in pattern recognition

Section CAttempt any one part of the following: 10 x 1 = 10

a
Explain the multivariate norma l distribution and its signifi cance in pattern recognition
b
Discuss how the chi-squared te st is applied for feature sele ction in a pattern recognition problem
a
Explain discriminant functions and how they determine decisi on boundaries
b
Using a suitable example, disc uss how Bayesian classification is applied to a two-class problem with given priors and likelihoods
a
Explain the Expectation-Maximization (EM) algorithm for inco mplete data problems using suitable examples
b
Illustrate through suitable e xamples how Principal Component Analysis (PCA) is used to reduce a dataset from 3D to 2D
a
Explain the working principl e of the K-Nearest Neighbour (K- NN) rule and its advantages using a suitable example
b
Discuss how to apply Parzen w indow density estimation to a 1 D dataset with three sample points
a
Using suitable examples and di agrams, explain the working of t h e K - means clustering algorithm with its limitations
b
Analyze the different linkage criteria used in agglomerative hierarchical clustering and explain how the choice of linkage affects the sh ape and structure of the resulting clusters. Support your analysis with suitable examples

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

Repeated Questions — KCA033

Questions that appeared in more than one session, found by comparing 2 years of Pattern Recognition papers (2021-22, 2024-25)

Describe the basic design p rinciples of a pattern recognition system

Appeared in: 2021-22 · 2024-25

Pattern Recognition — Other Year Papers

AKTU Pattern Recognition PYQs from other sessions

Pattern Recognition2021-22

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