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Printed Pages: 02
Paper Id: 11821
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Sub Code: NCS-080
Roll No.
Time: 3 Hours
B TECH
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(SEM. VIII) THEORY EXAMINATION 2017-18
PATTERN RECOGNITION
Attempt all Sections. If require any missing data; then choose suitably.
Total Marks: 100
SECTION A
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Attempt all questions in brief. 2 x 10 = 20
- a. Define the law of total probability.
- b. What do you mean by dimension reduction in pattern recognition?
- c. Write the difference between supervised learning and unsupervised learning.
- d. How do we evaluate the performance of a classifier?
- e. What is Hidden Markov Model (HMM)?
- f. What is discriminant function?
- g. Write short notes on Gaussian mixture model.
- h. Discuss cluster validation.
- i. Write K-means clustering algorithm.
- j. Write the difference between clustering and classification.
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SECTION B
Attempt any three of the following: 10 x 3 = 30
- a. What do you mean by learning and adaptation? Explain the components of a learning system.
- b. Explain the Chi-Square test and discuss their significance in pattern recognition with suitable example.
- c. Explain the concept of expectation maximization with the help of an algorithm.
- d. How K-nearest neighbor (KNN) method works? Explain with KNN estimation and KNN rule.
- e. Explain Naïve Bayes Classifier.
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SECTION C
Attempt any one part of the following: 10 x 1 = 10
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- (a) Consider a two-class problem where the classes are labeled pen drive and laptop. Suggest a set of features that could be used to discriminate between these two classes of objects.
- (b) What is Baysian Decision Theory? Discuss two-class category Classification in details.
Attempt any one part of the following: 10 x 1 = 10
- (a) Illustrate statistical and syntactic pattern recognition (SPR) approach.
- (b) Explain normal density function and discuss its significance in pattern recognition?
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Attempt any one part of the following: 10 x 1 = 10
- (a) Write short notes on following-
- i. Maximum likelihood estimation
- ii. Bayesian Estimation
- What is Parzen window? Explain. Derive the conditions for (i) convergence of mean (ii) convergence of variance.
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Attempt any one part of the following: 10 x 1 = 10
- (a) What do you mean by Fuzzy Decision making? Also discuss the Fuzzy Classification using suitable example.
- (b) Name the different methods of non-parameter estimation strategies. What are the main differences between them?
Attempt any one part of the following: 10 x 1 = 10
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- (a) What are different clustering techniques? Why is clustering important? What is an agglomerative clustering algorithm? Explain.
- (b) Write short notes on
- i. Cluster validation
- ii. Criteria function for clustering
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