Pattern Recognition (CS-503 (B)) - Important Questions

Explain the various design principles of pattern recognition systems.

Differentiate between supervised learning and unsupervised learning with suitable examples.

Write a detailed note on Random Forest.

Define the following: Pattern, Feature, Classifier and Metric Space.

How does K-Nearest Neighbour work? Explain with KNN estimation algorithm and KNN decision rules.

What do you understand by normalization? Explain with example.

Describe about cluster validation.

Enlist the clustering techniques. Explain any one unsupervised learning / clustering algorithm with an example - K-means.

What is pattern classification and what are the major paradigms of pattern recognition?

Discuss the various types of feature extraction methods.

Illustrate Branch and Bound algorithm with suitable example.

Write an algorithm for sequential forward selection with suitable example.

Describe Neuro-Fuzzy and its techniques.

State and explain the rules of Histogram in detail.

Write a short note on any two: i) Training set ii) Test set iii) Validation set