Download JNTUA (JNTU Anantapur) B.Tech R13 (Bachelor of Technology) 4th Year 2nd Semester 2018 April 13A04805 Pattern Recognition and Application Previous Question Paper || Download B-Tech 4th Year 2nd Sem 13A04805 Pattern Recognition and Application Question Paper || JNTU Anantapur B.Tech 4-2 Previous Question Paper || JNTU Anantapur B.Tech ME 4-2 Previous Question Paper || JNTU Anantapur B.Tech CSE 4-2 Previous Question Paper || JNTU Anantapur B.Tech Mech 4-2 Previous Question Paper || JNTU Anantapur B.Tech EEE 4-2 Previous Question Paper || JNTU Anantapur B.Tech ECE 4-2 Previous Question Paper

Code: 13A04805

B.Tech IV Year II Semester (R13) Regular & Supplementary Examinations April 2018

PATTERN RECOGNITION & APPLICATION

(Electronics and Communication Engineering)

Time: 3 hours Max. Marks: 70

PART ? A

(Compulsory Question)

*****

1 Answer the following: (10 X 02 = 20 Marks)

(a) List any three applications of pattern recognition.

(b) What are the basic steps in pattern recognition?

(c) On which Bayesian Decision theory is based?

(d) Explain symmetrical or zero-one loss function.

(e) List the two issues that come up for the classification accuracy with respect to dimensionality.

(f) Explain Rayleigh distribution along with their sufficient statistics.

(g) Explain a Discriminant function.

(h) What is the difference between Ho-Kashyap and modified Ho-Kashyap algorithm?

(i) List any two applications of HMM.

(j) Explain the sum-of-squared error criterion for clustering.

PART ? B

(Answer all five units, 5 X 10 = 50 Marks)

UNIT ? I

2 Explain the following terms associated with pattern recognition: (i) Sensing. (ii) Segmentation and

grouping (along with flow chart). (iii) Mereology.

OR

3

Explain the concepts of supervised learning, unsupervised learning and reinforcement learning using

suitable examples.

UNIT ? II

4 Explain the importance of Bayesian decision theory in pattern recognition problems by taking proper

examples.

OR

5 Explain how the action of a linear transformation on the feature space will convert an arbitrary normal

distribution into another normal distribution.

UNIT ? III

6 Explain how density estimation is useful in Non parametric pattern classification.

OR

7 Explain the conjunction rule of fuzzy classification and give the Cox-Jaynes axioms. List the four

limitations of fuzzy classification technique.

UNIT ? IV

8 Explain in detail about back propagation algorithm with respect to network learning, training protocols

and learning curves.

OR

9 Show for the Widrow-Hoff or LMS rule that: if then the sequence of weight vectors

converges to a limiting vector ?a? satisfying Y

t

(Y

a

- b) = 0.

UNIT ? V

10 Explain Graph-theoretic method of unsupervised classification using neat diagrams.

OR

11 Explain the forward-backward algorithm of HMM along with the equations relating to estimates.

*****

R13

FirstRanker.com - FirstRanker's Choice

This post was last modified on 10 September 2020