- Departments / Centres
Pre-Requisite : EC301
Contact Hours and Credits : ( 3 -0- 0 ) 3
To develop the mathematical tools required for the pattern recognition.
Fundamental concepts and blocks of a typical pattern recognition system. Decision functions- role and types, pattern and weight space, properties and implementation of decision functions.
Feature identification, selection and extraction. Distance measures, clustering transformation and feature ordering, clustering in feature selection, feature selection through maximization and approximations.
Pattern classification by distance functions. Clusters and cluster seeking algorithms. Pattern classification by likelihood functions. Baye’s classifier and performance measures.
Artificial neural network model, Neural network-based pattern associators, Feed forward networks and training by back-propagation- ART networks.
Applications of statistical and neural network – based pattern classifiers in speech recognition, image recognition and target recognition.
On the successful completion of this course Student are able