| Abstract: |
COURSE OUTLINE
Introduction and mathematical preliminaries ' What is pattern recognition?, Clustering vs. Classification; Applications; Linear Algebra, vector spaces, probability theory, estimation techniques.
Classification: Bayes decision rule, Error probability, Error rate, Minimum distance classifier, Mahalanobis distance; K'NN Classifier, Linear discriminant functions and Non'linear decision boundaries.
Fisher’s LDA, Single and Multilayer perceptron, training set and test sets, standardization and normalization.
Clustering: Different distance functions and similarity measures, Minimum within cluster distance criterion, K'means clustering, single linkage and complete linkage clustering, MST, medoids, DBSCAN, Visualization of datasets, existence of unique clusters or no clusters.
Feature selection: Problem statement and Uses, Probabilistic separability based criterion functions, interclass distance based criterion functions, Branch and bound algorithm, sequential forward/backward selection algorithms, (l,r) algorithm.
Feature Extraction: PCA, Kernel PCA.
Recent advances in PR: Structural PR, SVMs, FCM, Soft'computing and Neuro'fuzzy.
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Electronics & Communication Engineering
Pattern Recognition
