ITR: From Bits to Information: Statistical Learning Technologies for Digital Information Management and Search

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Modern statistical learning approaches are expected to play a key role in providing more powerful tools to harvest information from bits, a crucial and growing problem for the Internet. The goal of this project is thus to develop a new technology for the management, organization, and search of multimedia digital information by exploiting and extending new statistical learning theories and algorithms. In the process we expect to prototype key system components and to develop scientific insights. Anticipated outcomes of the research are (1) new learning algorithms and associated representations that can be applied to categorize text, images, and video, (2) new theoretical analyses of these learning algorithms and query-answering methods and (3) demonstrations and evaluations of prototype systems for classifying and routing email messages and searching, categorizing, and extracting information on the Web.Smarter classification software for multimedia data is a prerequisite to enable a second, more intelligent wave of Internet technologies. Automatic techniques to route, organize and search information are needed to help individuals and organizations exploit the sea of data that the computer networks are creating. The success of projects like this will make such a step possible and accelerate the evolution of the Internet.