Download AKTU (Dr. A.P.J. Abdul Kalam Technical University (AKTU), formerly Uttar Pradesh Technical University (UPTU) B-Tech 8th Semester (Eight Semester) 2018-19 NCS080 Pattern Recognition Question Paper
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B.TECH.
(SEM VIII) THEORY EXAMINATION 2018-19
PATTERN RECOGNITION
T ime: 3 Hours T 0tal Marks: 100
Note: 1. Attempt all Sections. If require any missing data; then choose suitably.
SECTION A
1 Attempt all questions in brief. 2 x 10 = 20
a. What is a prior probability?
b. What are the important components of Learning?
0 Consider a tetrahedral die and roll it twice. What is the probability that the number on
the first roll is strictly higher than the number on the second roll?
Note: A tetrahedral die has only four sides(l,2,3 and 4).
What is the purpose of a goodness-of?fit test?
e. What do you mean by cluster analysis?
f. A roulette Wheel has 38 slots?18 red,l8 black, and 2 green. You play five games and
always bet on red slots. What is the probability that you win all the five games?
g. What is machine Learning? Why do we need Machine Learning?
h. In case of equal discriminant function, pattern is assigned to which class? Also give the
optimal discriminant function for two?class case.
i. What do you mean by defuzzification?
j. Define expectation and mean.
SECTION B
2. Attempt any three of the following: 10x3=30
a. What are the different clustering techniques? Explain. Also explain agglomerative
clustering algorithm step by step.
b. What are dimension reduction methods? Explain Principle Component Analysis
algorithm for dimension reduction, Also write its limitations.
0. Write an algorithm for k-nearest neighbor estimation. Explain.
(1. Explain Supervised and unsupervised Learning algorithms using a block diagram. Is
clustering considered an unsupervised learning? J ustify.
e. What is Bayesian decision theory? Explain 2 category classification.
SECTION C
3. Attempt any one of the following: 10x1=10
a. What do you mean by fuzzy decision making? Also discuss the fuzzy classification
using suitable examples in detail.
b. Write short note on:
i)Reinforcement learning
ii)Expectation Maximization
4. Attempt any one of the following: 10x1=10
a. Explain components of a typical pattern recognition system with a neat diagram.
b. Explain multivariate normal density using mathematical notations.
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5. Attempt any one of the following: 10x1=10
a. Explain Chi square test. How Chi?square test can be applied for testing null
hypothesis?
The following table gives the number of suicides in Saskatchewan from 1978? 1989:
Year No of Suicides in Saskatchewan
1978 164
1979 142
1980 153
1981 171
1982 171
1983 148
1984 136
1985 133
1986 138
1987 132
1988 145
1989 124
Use the Chi square Test for goodness of fit to test the hypothesis that the number of
suicides reported for each year from 1978?1989 does not differ significantly from an
equal number of suicides in each year.
b. Explain iterative squared-error partitional clustering. Write its algorithm also.
6. Attempt any one of the following: 10x1=10
a. Explain Hidden Markov model. How Hidden Markov model is different from
traditional markov model? Explain.
b. Explain the convergence of parzen window.
7. Attempt any one of the following: 10x1=10
a. Explain Naives Bayes classifier. Consider a fictional dataset that describes the
weather conditions for playing a game of golf. Given the weather conditions, each
tuple classifies the conditions as ?t(?Yes?) 0r unfit(?No?) for playing golfHere is a
tabular representation of our dataset:
Outlook Temperature Humidity Windy Play Golf
Rainy Hot High False N0
Rainy Hot High True N0
Overcast Hot High False Yes
Sunny Mild High False Yes
Sunny Cool Normal False Yes
Sunny Cool Normal True No
Overcast Cool Normal True Yes
Rainy Mild High False N0
Rainy Cool Normal False Yes
Sunny Mild Normal False Yes
Rainy Mild Normal True Yes
Overcast Mild High True Yes
Overcast Hot Normal False Yes
Sunny Mild High True N0
Test it on a new set of features(Lets call the set of features today):
today=(Sunny,H0t,N0rmal,False) and determine whether the golf will be played or
not.
b. Explain probabilistic neural network with its benefits.
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This post was last modified on 30 January 2020