Pattern Recognition (CS-503)
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B.Tech RGPV notes AICTE flexible curricula Bachelor of technology
Syllabus
UNIT 1:
Introduction – Definitions, data sets for Pattern, Application Areas and Examples of
pattern recognition, Design principles of pattern recognition system, Classification
and clustering, supervised Learning, unsupervised learning and adaptation, Pattern
recognition approaches, Decision Boundaries, Decision region , Metric spaces,
distances.
UNIT 2:
Classification: introduction, application of classification, types of classification,
decision tree, naïve bayes, logistic regression , support vector machine, random forest,
K Nearest Neighbour Classifier and variants, Efficient algorithms for nearest
neighbour classification, Different Approaches to Prototype Selection, Combination
of Classifiers, Training set, test set, standardization and normalization.
UNIT 3:
Different Paradigms of Pattern Recognition, Representations of Patterns and Classes,
Unsupervised Learning & Clustering: Criterion functions for clustering, Clustering
Techniques: Iterative square -error partitional clustering – K means, hierarchical
clustering, Cluster validation.
UNIT 4:
introduction of feature extraction and feature selection, types of feature extraction ,
Problem statement and Uses, Algorithms - Branch and bound algorithm, sequential
forward / backward selection algorithms, (l,r) algorithm.
UNIT 5:
Recent advances in Pattern Recognition, Structural PR, SVMs, FCM, Soft computing
and Neuro-fuzzy techniques, and real-life examples, Histograms rules, Density
Estimation, Nearest Neighbor Rule, Fuzzy classification.
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Books Recommended
1. Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification”, 2nd
Edition, John Wiley, 2006.
2. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2009.
3. S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, 4th Edition, academic
Press, 2009.
4. Robert Schalkoff, “pattern Recognition: statistical, structural and neural approaches”,
JohnWiley & sons , Inc, 2007.
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