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Tuesday, February 29, 2000

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UAI-2000 Workshop on Fusion of Domain Knowledge with Data for Decision Support
"Statistics and inductive machine learning are data-oriented tasks in which domain models are induced from data, but databases are not the only resource from which models can be derived. Domain knowledge is still an important resource (whether in the form of deep knowledge or heuristics), and refinements in knowledge engineering have made the construction of knowledge-based systems more tractable. But when both data and domain knowledge are available, how can these two resources be used together for the construction of decision support systems? There is a growing interest in developing systems capable of both representing the knowledge pertaining to a domain and learning from the data available. Formal frameworks that enable some form of knowledge-data fusion (a.k.a. theory revision; theory refinement) to take place include probabilistic networks, possibilistic networks, neuro-fuzzy systems, and statistical inductive logic programming. A one-day workshop on knowledge-data fusion will be held on June 30, 2000, at Stanford University as part of The Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000). "

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Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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