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2005
Conference Paper
Titel
From bits and bytes to information and knowledge
Abstract
Unstructured data is a valuable source of information and implicit knowledge. Yet, the bits and bytes of, e.g., text, image, or click-stream data need to be interpreted in order to transform them into business intelligence and actionable information. Clearly, this process needs to be automated to the largest possible extend in order to be scalable to the typical volumes of data. One way to accomplish this is through the use of machine learning and statistical modeling techniques. This talk will provide an overview of recent progress and new trends in machine learning and discuss their relevance for developing intelligent tools for search, information filtering, categorization, and knowledge extraction.