AP7013 PATTERN RECOGNITION

AP7013 PATTERN RECOGNITION

UNIT I PATTERN CLASSIFIER

Overview of Pattern recognition – Discriminant functions – Supervised learning – Parametric estimation – Maximum Likelihood Estimation – Bayesian parameter Estimation – Problems with Bayes approach– Pattern classification by distance functions – Minimum distance pattern classifier. 

UNIT II CLUSTERING

Clustering for unsupervised learning and classification – Clustering concept – C Means algorithm – Hierarchical clustering – Graph theoretic approach to pattern Clustering – Validity of Clusters.

UINT III FEATURE EXTRACTION AND STRUCTURAL PATTERN RECOGNITION

KL Transforms – Feature selection through functional approximation – Binary selection -Elements of formal grammars - Syntactic description - Stochastic grammars - Structural representation. . 

UNIT IV   HIDDEN MARKOV MODELS AND SUPPORT VECTOR MACHINE

State Machines – Hidden Markov Models – Training – Classification – Support vector Machine – Feature Selection.

UNIT V     RECENT ADVANCES

Fuzzy logic – Fuzzy Pattern Classifiers – Pattern Classification using Genetic Algorithms – Case Study Using Fuzzy Pattern Classifiers and Perception.

REFERENCES: 

1.  M. Narasimha Murthy and V. Susheela Devi, “Pattern Recognition”, Springer 2011. 

2. S.Theodoridis and K.Koutroumbas, “Pattern Recognition”, 4th Ed., Academic Press, 2009. 

3.  Robert J.Schalkoff, “Pattern Recognition Statistical, Structural and Neural Approaches”, John Wiley & Sons Inc., New York, 1992. 

4.  C.M.Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006. 

5.  R.O.Duda, P.E.Hart and D.G.Stork, “Pattern Classification”, John Wiley, 2001 

6.  Andrew Webb, “Stastical Pattern Recognition”, Arnold publishers, London,1999.