Pattern Recognition and Neural Networks

Pattern Recognition encompasses a wide range of information processing problems of great practical significance, ranging from classification of handwritten or printed text to multimedia -- video and audio -- indexing and retrieval and data mining.  The approach we follow is hybrid and involves both statistical pattern recognition based on the probabilistic nature of the data  and machine learning techniques characteristic of AI. Traditional neural (connectionist) computation including feedforward and recurrent networks,  radial basis  functions, and self-organization feature maps,  statistical learning theory and support vector machines,  and mixture of experts are just some of the approaches being used.

 Relevant Courses : CS 682, CS 750, INFT 844

 Faculty:   Peter Pachowicz,   Harry Wechsler