EEE 433 Pattern Recognition and Neural Network

Introduction: Basic concepts, Design concepts, Examples; Decision functions: Linear decision functions, Generalized decision functions; Pattern classification by distance functions: Minimum distance pattern classification, Cluster seeking; Pattern classification by likelihood functions: Bayes classifier; Structural pattern representation: Grammars for pattern representation, Picture description language and grammars, Stochastic grammars; Structural pattern recognition: String to string distance; Matching other structures: Relational structures, Graph matching, Matching by relaxation, Random graph. Elementary Neurophysiology – Biological Neurons to Artificial  Neurons. Adaline and the Medaline. Perceptron. Backpropagation  Network. Bidirectional Associative Memories. Hopfield Networks. Counterpropagation  Networks. Kohonen’s Self Organizing  Maps. Adaptive Resonance Theory. ART1 – ART2 – ART3. Boltzman Machines, Spatiotemporal Pattern Classifier, Neural Network models: Neocognitron, Application of Neural Networks to various disciplines.


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