Pattern Recognition

Supervised versus unsupervised classification. Bayesian classifiers, Gaussian distributed classes and nearest distance classifiers. Maximum likelihood and maximum aposteriori probability density estimators. Non-parametric density estimators, Parzen windows, k-nearest point estimators and classifiers. Naïve Bayes classifier, Bayesian networks. Linear classifiers, perceptron and the perceptron algorithm, Least Squares (LS) classifiers. Non-linear classifiers, multilayer perceptros, the back propagation algorithm. Feature generation, schemes for shape representation and description, chain codes, polygon description, signatures, Fourier transform descriptors, texture and moments.