# Pune University BE (Computer Engineering) Pattern Recognition Question Papers

B.E. (Computer) PATTERN RECOGNITION (2008 Pattern) (Semester – II) (Elective – III)

Time :3 Hours]                                                                           [Max. Marks :100

Instructions to the candidates :-

1)           Attempt: Q. 1 or Q. 2, Q. 3 or Q. 4, Q. 5 or Q. 6 from section I.

Attempt: Q. 7 or Q. 8, Q. 9 or Q. 10, Q. 11 or Q. 12 from section II.

2)            Answers to the two sections should be written in separate answers books.

3)           Neat diagrams must be drawn wherever necessary.

4)           Pigures to the right indicate full marks.

5)           Assume suitable data, if necessary.

SECTION – I

QI) a) Differentiate supervised learning and unsupervised learning.       

b) Explain the concept of feature extraction in pattern recognition system with examples.                                                                                                                       

OR

Q2) a) What are the problems arise by activities in design of pattern recognition System?                                                                                                                

b) Explain the concept of Classification and Post processing in pattern recognition.          

Q3) a) Write a short note on Minimum error rate classification.                

b) With the help of suitable diagram explain classifiers and functional structure of general statistical pattern classifier.                                          

OR

Q4) a) Explain the uni-variate and multivariate normal density functions with examples.                                                                                                             

b) What are challenges in Bayesian decision theory?                                           

Q5) a) Discuss the general principal of Maximum likelihood estimation.  b) Write a short note on General theory of Bayesian Parameter estimation. 

OR

Q6) a) Write Expectation Minimization (EM) algorithm. Explain EM for 2D normal model.                                                                                                                   

b) Illustrate a Gaussian mixture distribution in one dimension and also illustrate a mixture of three Gaussian in 2 dimensional space.                                                   

SECTION – II

Q7) a) Write HMM Decoding algorithm. With the help of example explain the state sequence decoding of hidden Markov model.                                               

b) Explain Principal Component Analysis (PCA) with analytical treatment. 

OR

Q8) a) Consider training and HMM by the forward and backward algorithm for a single sequence of length T where each symbol could be one of c values. What is the computational complexity of a single revision of all values aij and b ij.                                                                                              

b) Write a short note on Fisher-Linear Discriminant.                                          

Q9) a) Write a short note on support vector machine.                                 

b) Explain 2 category and multi category case of linear discriminant functions. Also explain linear decision bounding for 4 class problem with the help of suitable diagram.         

OR

QI0) a) Explain Parzen window approach for density estimation. State and explain examples of 2dimentional circularly symmetric normal Parzen window for 3 different values of h.                                                                                                          

b) Explain following scattered criteria’s with the help of suitable examples. 

i)              The scattered matrices ii) The trace criteria

i)             Determinant criteria iv) Invariant criteria

QII) a) When a test pattern is classified by a decision tree, that pattern is subjected to a sequence of queries, corresponding to the nodes along a path from root to leaf ? Prove that for any decision tree.                                                                                                        

b) Write algorithm for K-means clustering with the help of diagram. Explain how the K-means clustering produces a form of stochastic hill climbing in the log likelihood function.                                                                                                                        

OR

Q12) a) Write a short note on application of normal mixture.                                      

b) Explain following criteria functions for clustering :                                

i)              The sum of squared error.

ii)           Related minimum variance.