His speech, entitled "Probabilistic Graphical Models in Machine Learning," focused on the design of computer programs that learn and are able to modify their behavior in an environment of constantly changing information. Machine learning is crucial in fields such as document analysis and recognition due to the difficulty of expressing perceptual images, such as handwriting, in algorithms that computers can understand. Many second-generation machine learning programs were enabled by postal data collected at the Buffalo post office by UB CEDAR students. " Research by Srihari, his colleagues and students at CEDAR that allowed machines to recognize and understand handwriting was at the core of the first handwritten address-interpretation system used by the U.S.
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