Download AKTU B-Tech 8th Sem 2018-19 NCS080 Pattern Recognition Question Paper

Firstranker's choice

Printed Pages: 02

FirstRanker.com

--- Content provided by‌ FirstRanker.com ---

Paper Id: 110236

Roll No.

B.TECH.

(SEM VIII) THEORY EXAMINATION 2018-19

PATTERN RECOGNITION

--- Content provided by​ FirstRanker.com ---

Time: 3 Hours

Total 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

--- Content provided by‌ FirstRanker.com ---

1. What is a prior probability?
2. What are the important components of Learning?
3. 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(1,2,3 and 4).
4. What is the purpose of a goodness-of-fit test?

--- Content provided by‌ FirstRanker.com ---

5. What do you mean by cluster analysis?
6. A roulette wheel has 38 slots-18 red,18 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?
7. What is machine Learning? Why do we need Machine Learning?
8. In case of equal discriminant function, pattern is assigned to which class? Also give the optimal discriminant function for two-class case.
9. What do you mean by defuzzification?

--- Content provided by‍ FirstRanker.com ---

10. Define expectation and mean.

SECTION B

1. Attempt any three of the following: 10x3=30

1. What are the different clustering techniques? Explain. Also explain agglomerative clustering algorithm step by step.
2. What are dimension reduction methods? Explain Principle Component Analysis algorithm for dimension reduction. Also write its limitations.

--- Content provided by‍ FirstRanker.com ---

3. Write an algorithm for k-nearest neighbor estimation. Explain.
4. Explain Supervised and unsupervised Learning algorithms using a block diagram. Is clustering considered an unsupervised learning? Justify.
5. What is Bayesian decision theory? Explain 2 category classification.

SECTION C

1. Attempt any one of the following: 10x1=10

--- Content provided by‍ FirstRanker.com ---

1. What do you mean by fuzzy decision making? Also discuss the fuzzy classification using suitable examples in detail.
2. Write short note on:
   1. Reinforcement learning
   2. Expectation Maximization

1. Attempt any one of the following: 10x1=10

--- Content provided by​ FirstRanker.com ---

1. Explain components of a typical pattern recognition system with a neat diagram.
2. Explain multivariate normal density using mathematical notations.

FirstRanker.com

FirstRanker.com

Firstranker's choice

--- Content provided by FirstRanker.com ---

Printed Pages: 02

FirstRanker.com

1. Attempt any one of the following: 10x1=10

1. 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:
   

   --- Content provided by FirstRanker.com ---

   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.
2. Explain iterative squared-error partitional clustering. Write its algorithm also.

1. Attempt any one of the following: 10x1=10

1. Explain Hidden Markov model. How Hidden Markov model is different from traditional markov model? Explain.

--- Content provided by​ FirstRanker.com ---

2. Explain the convergence of parzen window.

1. Attempt any one of the following: 10x1=10

1. 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 fit(“Yes”) or unfit(“No”) for playing golf.Here is a tabular representation of our dataset:
   

   Outlook	Temperature	Humidity	Windy	Play Golf
   Rainy	Hot	High	False	No
   Rainy	Hot	High	True	No
   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	No
   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	No

   
   Test it on a new set of features(Lets call the set of features today):
   

   --- Content provided by‌ FirstRanker.com ---

   today=(Sunny, Hot,Normal,False) and determine whether the golf will be played or not.
2. Explain probabilistic neural network with its benefits.

FirstRanker.com