09AE 6122 Pattern Recognition and Analysis
Dear M.Tech Signal Processing students,
This is the course page for the course 09AE6122 pattern Recognition and Analysis
COURSE PLAN
Course No:09AE6122 Title: PATTERN RECOGNITION AND ANALYSIS
(L-T-P): 3-0-0 Credits : 3
Module I: Introduction - features, feature vectors and classifiers,
Supervised versus unsupervised pattern recognition. Classifiers based
on Bayes Decision theory- introduction, discriminant functions and
decision surfaces, Bayesian classification for normal distributions,
Estimation of unknown probability density functions, the nearest
neighbour rule. Linear classifiers,- Linear discriminant functions and
decision hyper planes, The perceptron algorithm, MSE estimation,
Logistic determination, Support Vector machines.
Contact Hrs :11, % Marks in sem exam= 25
Module II: Non-Linear classifiers- Two layer and three layer
perceptrons, Back propagation algorithm, Networks with Weight
sharing, Polynomial classifiers, Radial Basis function networks,
Contact Hrs :5, % Marks in sem exam= 13
FIRST INTERNAL TEST
Support Vector machines- nonlinear case, Decision trees, combining
classifiers, Feature selection, Receiver Operating Characteristics
(ROC) curve, Class separability measures, Optimal feature
generation, The Bayesian information criterion.
Contact Hrs :5, % Marks in sem exam= 12
Module III: Feature Generation 1- Linear transforms- KLT, SVD,
ICA, DFT, DCT, DST, Hadamard Transform, Wavelet Transform,
Wavelet Packets etc- Two dimensional generalizations -
Applications. Feature Generation 2- regional features, features for
shape and characterization, Fractals, typical features for speech and
audio classification, Template Matching, Context dependent
classification-Bayes classification, Markov chain models, HMM,
Viterbi Algorithm. System evaluation - Error counting approach,
Exploiting the finite size of the data.
Contact Hrs :11, % Marks in sem exam= 25
SECOND INTERNAL TEST
Module IV: Clustering - Cluster analysis, Proximity measures,
Clustering Algorithms - Sequential algorithms, Neural Network
implementation. Hierarchical algorithms - Agglomerative algorithms,
Divisive algorithms. Schemes - based on function optimization -
Fuzzy clustering algorithms, Probabilistic clustering, K - means
algorithm. Clustering algorithms based on graph theory - Competitive
learning algorithms, Binary Morphology Clustering Algorithms
Boundary detection methods, Valley seeking clustering, Kernel
clustering methods. Clustering validity.
Contact Hrs :10 , % Marks in sem exam=25
END SEMESTER EXAMINATION