• Attempt any five questions.
• All questions carry equal marks.
Explain various Design principles of Pattern Recognition systems.
Differentiate between supervised learning and unsupervised learning with suitable example.
Write a detail note on Random Forest.
Define the following
i) Clustering
ii) Metric spaces
How does K-Nearest Neighbor works? Explain with KNN estimation and KNN rules.
What do you understand by normalization? Explain with example.
Describe about cluster validation.
Enlist the Clustering Techniques. Explain any one Technique.
State and explain the different paradigms of pattern recognition.
Discuss the various types of feature extraction.
Illustrate branch and bound Algorithm with suitable example.
Write an algorithm for forward selection with suitable example.
Describe about the of Neuro-Fuzzy and its Techniques.
State and explain the rules of Histogram in detail.
Write a short note on: Data sets for pattern
Write a short note on: Training set
Write a short note on: Classifier and variants
Write a short note on: FCM
