MAESP206-4 Pattern Recognition & Analysis M.Tech Model Question Paper : mgu.ac.in

Name of the University : Mahatma Gandhi University
Department : Applied Electronics and Instrumentation Engineering
Degree : M.Tech
Subject Code/Name : MAESP206-4/Pattern Recognition And Analysis
Sem : II
Website : mgu.ac.in
Document Type : Model Question Paper

Download Model/Sample Question Paper : https://www.pdfquestion.in/uploads/mgu.ac.in/5236-MAESP%20206_4%20Pattern%20%20Recognition%20and%20Analysis(1).doc

Pattern Recognition & Analysis Question Paper :

M.TECH Degree Examination :
Second Semester :
Branch: Applied Electronics and Instrumentation Engineering

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Specialization: Signal Processing
MAESP 206 – 4 Pattern Recognition And Analysis (Elective IV)
(2011 Admission onwards)
MODEL QUESTION PAPER :
Time: Three Hours
Maximum: 100 Marks
1) (a) Describe the basic modules in designing a pattern recognition system. (7)
(b) State the Bayes Rule and explain how it is applied to pattern classification problems. Show that in a multiclass classification task the Bayes decision rule minimizes the error probability (15)
(c) Briefly explain what is generalization in the context of pattern recognition problems? (3)
OR

2) (a) Draw the diagram single layer two input – one output perceptron. State its weight update equation. (5)
(b) Show that the perceptron weight update algorithm converges to a solution after a finite number of iteration if the training data set is linearly separable. (10)

(c) Given the equation for a line s1 + s2 – 0.5 = 0, the weight vector w = [1 1 -0.5]T. Data vectors [0.4 0.05]T belonging to y1 (target = +1) and [-0.2 0.75]T belonging to y2 (target = -1) are misclassified. Calculate the weight vector after the first iteration. The learning rate ? = 0.7. (10)

3) (a) Show the design of a two layer perceptron to solve the XOR problem in a 2- D input feature space. (8)
(b) Explain that a perceptron with J hidden units an I-dimensional input space is mapped onto the vertices of a hypercube made by J hyperplanes. (7)
(c) Show that a three layered perceptron can perform any logical combination of convex regions. (10)
OR

4) (a) Why is back propagation algorithm so called? What is the significance of its activation function in relation to its cost function? (7)

(b) With multilayer networks what is the limitation of the least squares cost function and suggest an alternate cost function with appropriate equations that is better suited for pattern recognition tasks and indicate its advantages. (8)
(c) Discuss the solution of XOR problem using a polynomial classifier. (10)

5) (a) Discuss qualitatively that for data not linearly separable in the input feature space there always exists a nonlinear mapping into higher dimensional space that makes it linearly separable. (5)

(b) For a support vector machine how is the dependency on the weight vector in the primal space eliminated by recasting the optimization problem in the dual space and explain the method of finding the optimal hyperplane corresponding to its optimal weight vectors. (15)

(c) Write a short note with diagrams on Decision trees which are nonlinear, nonmetric classifiers. (5)
OR

6) (a) What is the advantage of combined model of classifiers? With a diagram show how L classifiers can be combined to solve a pattern classification problem. (5).

(b) In a one-dimensional feature space with P(y1) = P(y2) with Gaussian distributions, consider Case-1 with s1 = 10s2 and Case-2 with s1 = 100s2 where s1, s2 are the variances of the two classes. Calculate the Bhattacharyya distances for the two cases and show that greater the differences between the variances the smaller the error bound. (10).

(c) Consider a two class case and show that the optimal direction of the weight vector w along which the two classes are best separated is obtained by maximizing the Fisher’s criterion. (10)

7) (a) Describe the basic steps that must be followed in order to develop a clustering task. (8)
(b) Write the code for Basic Sequential Algorithm Scheme. State whether number of clusters are known a priori in case of BSAS (10).

(c) Which are the two schemes of Hierarchical clustering algorithm? Give brief descriptions. (7)
OR
8) (a) To which category of clustering schemes does the k-means algorithm belong? What is its major advantage? Which are the factors that influence the computational duration of this algorithm? (10)

(b) With a diagram explain the Minimum Spanning tree algorithm. (7)
(c) Describe the basic competitive learning algorithm with relevant equations. (8)