Patern Recognition

Methods and pattern recognition systems, Limitation in accuracy of recognition reliability, Guided learning and self-learning, Distance Functions. Linear and non-linear decision functions, Perceptron Algorithm, Bayes Classifiers, Nearest neighbor classifiers, Parametric and non-parametric estimation of probability density models: Maximizing entropy, Parzen estimator, orthonormal functions, Robbins Monro and Kiefer Wolfowitz methods, LMS, Least squares Methods., Multilayer artificial neural networks, Recursive artificial neural networks, Error correction training, Hebbian and competitive training, Multilayer perceptron, Error Back Propagation, Radial basis function networks, Hopfield machine, supervised and unsupervised learning, Hierarchical data clustering, Fuzzy logic, Genetic algorithms and evolutionary computation principles.
Code Hours Type eClass Semester
ΗΥ670 4 Specialisation H.I. e-Class 6

bibliography:

  • “Pattern Classification, R. O. Duda, P.E. Hart and D.G. Stork, Wiley, 2001.”
  • “Pattern Recognition, S. Theodoridis and K. Koutroumbas, Elsevier Acad. Press, 2006.”
  • “Pattern Recognition and Machine Learning, CM Bishop, Springer, 2006.”
  • “Pattern Recognition and Machine Learning, C. Karagiannis and C. Steinhauer, NTUA, 2001.”