Download JNTUA B.Tech 4-2 R13 2018 April 13A04805 Pattern Recognition and Application Question Paper

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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
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