Efficient Algorithms for Learning and Reasoning from Data

该研究的目标是开发高效，可扩展和可移植的算法，允许对非常大的数据库进行学习和有限的推理。 目标是将联合收割机这些算法组合到一个环境中，该环境支持对非常大的数据流进行汇总（数据挖掘），并在适当的情况下使用概率推理从这些汇总中得出合理准确的结论。 正在调查以下领域： (1)概率网络中推理的有效算法 (2)可证明准确的广义决策树的有效归纳;（3）基于记忆的推理方法中的知识表示。 特别感兴趣的是推理算法，执行查询和更新数据非常有效，也许在时间上是次线性的原始数据库的大小，和学习算法，提供性能保证的答案的准确性，给出一个置信度评级的答案查询。 用于从数据中学习和推理的高速、可靠、智能算法的可用性将在数字图书馆、医疗保健、过程控制和资源分配等领域中具有重要应用。

The research has as its goal the development of efficient, scalable and portable algorithms that allow learning and limited reasoning over very large repositories of data. The goal is to combine these algorithms into an environment that supports summarization of very large streams of data (data mining) and the drawing of reasonably accurate conclusions from these summaries, using probabilistic reasoning where appropriate. The following areas are being investigated: (1) Efficient algorithms for reasoning in probabilistic networks; (2) Efficient induction of provably accurate generalized decision trees; and (3) Incorporating knowledge in memory-based reasoning methods. Of particular interest are reasoning algorithms that perform queries and updates to data extremely efficiently, perhaps in time which is sublinear to the size of the original database, and learning algorithms that provide performance guarantees on the accuracy of the answers by giving a confidence rating with the answer to a query. The availability of high-speed, reliable, intelligent algorithms for learning and reasoning from data will have important applications in digital libraries, medical care, process control, and resource allocation, among other areas.