10CS664: PATTERN RECOGNITION QUESTION BANK

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1 10CS664: PATTERN RECOGNITION QUESTION BANK Assignments would be handed out in class as well as posted on the class blog for the course. Please solve the problems in the exercises of the prescribed text book for practice. (Selected problems will be solved in class and assigned as homework.) In addition to these, you may use the following questions for your review. Unit 1 1. What does the design of a generic pattern recognition system involve? 2. With an example, explain what is meant by feature extraction? 3. Why is it preferred to extract features instead of working with raw data? 4. What is meant by training a classifier? 5. How can classifiers be evaluated? Unit 2 1. What is the Bayes decision rule? Describe the input necessary to arrive at a Bayes Decision rule for a given data set. 2. What is the Bayes Decision theory for continuous features? Given an example where the feature space would be continuous and such a decision theory is useful. 3. What is meant by the likelihood ratio? 4. What is the loss function? How can it be factored into the Bayes decision theory? 5. In the context of the Bayes decision theory, what are the consequences, if any, of assuming statistical independence of the elements of the feature vector? Unit 3 1. Explain the general principal of the maxium-likelihood estimation (assuming a Gaussian distribution of the features) for the following cases a. unknown mean, known co-variance matrix b. unknown mean and unknown co-variance matrix 2. How is Bayesian parameter estimation done in the Gaussian case if the Gaussian is a. Univariate b. Multivariate 3. Write a note on the first-order hidden markov models. 1

2 4. Explain the forward-backward algorithm with a suitable example. Unit 4 1. How can the Parzen window method be used to estimate the densities? 2. Explain how the kn-nearest neighbour method works. 3. What is meant by a metric? Discuss any two metrics that can be used as a metric for the nearest-neighbour method. Unit 5 1. What are linear discriminant functions? What do they look like in higherdimensions? 2. How does the basic gradiant decent procedure work? 3. What is the perceptron criterion function? How can it be minimized? 4. What are relaxation procedures? Discuss any one of them. 5. What is meant by a pseudoinverse? How is it used in MSE? 6. What are the steps of the Ho-Kashyap algorithm? Unit 6 1. What is meant by simulated annealing? 2. How does the simulated annealing algorithm work? 3. How does Boltzmann training deal with missing features and category constraints? 4. How are graphical models used for pattern classification? 5. Briefly explain how a genetic algorithm works. Unit 7 1. What are decision trees? 2. Describe any two types of node impurities. 3. How does one decide when to stop splitting a decision tree? 4. How are the nodes in a tree pruned? 5. How do the following string matching algorithms work? a. Naïve string matching b. Boyer-moore string matching 6. Write a note on the use of grammatical methods in pattern recognition. 2

3 Unit 8 1. Distinguish between supervised and unsupervised learning. When is the latter a preferred technique? 2. Describe the k-means clustering algorithm. 3. What do you mean by Fuzz k-means? 4. Discuss what similarity measures are and how they can be designed. 5. What is meant by hierarchical clustering? 6. How are unsupervised learning methods validated? 3

4 PATTERN RECOGNITION PAPER DECEMBER 2011 PART A 1. Discuss two approaches of pattern recognition Differentiate between linear and non-linear decision boundary Explain the components of a typical pattern recognition system. 4. Discuss various types of learning Define generalized Bayes decision theory Stating the initial assumptions, obtain expressions for MAHALNOBIS distance measure. 7. Show that p(error) is least when the decision boundary is taken at a point,where 10 p(x/w1)p(w1)=p(x/w2)p(w2) 8. List the assumptions made in maximum likelihood estimation. Explain the concept of maximum likelihood estimation. 9. Differentiate between parametric and non parametric techniques Determine maximum likelihood estimation of mean. The guassian case :unknown µ 11. Discuss Fisher s Linear discriminant with an illustration. 12. Explain hidden markov models. 04 PART B 1. Discuss the concept density estimation. Explain Parzen window and kn nearest neighbour method. 2. Describe k-nearest neighbour rules of classification Explain ISODATA procedure Explain Boltzmann learning. 5. Discuss how CART can be used for classification with an example. 6. Enumerate the applications of clustering How are similarity and dissimilarity between two samples measured? 8. Explain with suitable example : 1) single linkage method 4

5 2) Complete linkage method 3) Average linkage method. 9. Explain minimum square error criterion method of non-hierarchical clustering. 10. Write short notes on a) Ho-Kashyap procedures b) Relaxation procedures c) Minimum error rate classifier d) Recursive Bayes approach. 5